    [Senate Hearing 118-732]
    [From the U.S. Government Publishing Office]

                                                            S. Hrg. 118-732

                         THE NEED TO PROTECT AMERICANS' PRIVACY
                                    AND THE AI ACCELERANT

    =======================================================================

                                    HEARING

                                   BEFORE THE

                             COMMITTEE ON COMMERCE,
                          SCIENCE, AND TRANSPORTATION
                              UNITED STATES SENATE

                        ONE HUNDRED EIGHTEENTH CONGRESS

                                 SECOND SESSION

                                   __________

                                 JULY 11, 2024

                                   __________

        Printed for the use of the Committee on Commerce, Science, and
                                 Transportation

    [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

                    Available online: http://www.govinfo.gov

                                   __________

                       U.S. GOVERNMENT PUBLISHING OFFICE
    61-871 PDF                  WASHINGTON : 2025

    -----------------------------------------------------------------------------------

           SENATE COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION

                        ONE HUNDRED EIGHTEENTH CONGRESS

                                 SECOND SESSION

                       MARIA CANTWELL, Washington, Chair
    AMY KLOBUCHAR, Minnesota             TED CRUZ, Texas, Ranking
    BRIAN SCHATZ, Hawaii                 JOHN THUNE, South Dakota
    EDWARD MARKEY, Massachusetts         ROGER WICKER, Mississippi
    GARY PETERS, Michigan                DEB FISCHER, Nebraska
    TAMMY BALDWIN, Wisconsin             JERRY MORAN, Kansas
    TAMMY DUCKWORTH, Illinois            DAN SULLIVAN, Alaska
    JON TESTER, Montana                  MARSHA BLACKBURN, Tennessee
    KYRSTEN SINEMA, Arizona              TODD YOUNG, Indiana
    JACKY ROSEN, Nevada                  TED BUDD, North Carolina
    BEN RAY LUJAN, New Mexico            ERIC SCHMITT, Missouri
    JOHN HICKENLOOPER, Colorado          J. D. VANCE, Ohio
    RAPHAEL WARNOCK, Georgia             SHELLEY MOORE CAPITO, West
    PETER WELCH, Vermont                     Virginia
                                         CYNTHIA LUMMIS, Wyoming
                       Lila Harper Helms, Staff Director
                     Melissa Porter, Deputy Staff Director
                         Jonathan Hale, General Counsel
                     Brad Grantz, Republican Staff Director
               Nicole Christus, Republican Deputy Staff Director
                         Liam McKenna, General Counsel

                                C O N T E N T S

                                  ----------
                                                                       Page
    Hearing held on July 11, 2024....................................     1
    Statement of Senator Cantwell....................................     1
    Statement of Senator Cruz........................................     3
    Statement of Senator Wicker......................................    43
    Statement of Senator Rosen.......................................    45
    Statement of Senator Blackburn...................................    47
    Statement of Senator Hickenlooper................................    48
    Statement of Senator Moran.......................................    50
    Statement of Senator Budd........................................    53
    Statement of Senator Klobuchar...................................    55
    Statement of Senator Vance.......................................    57
    Statement of Senator Schmitt.....................................    58
    Statement of Senator Welch.......................................    61
    Statement of Senator Thune.......................................    63

                                   Witnesses

    Ryan Calo, Lane Powell and D. Wayne Gittinger Professor of Law,
      University of Washington.......................................     5
        Prepared statement...........................................     7
    Amba Kak, Co-Executive Director, AI Now Institute................    11
        Prepared statement...........................................    12
    Udbhav Tiwari, Director, Global Product Policy, Mozilla..........    21
        Prepared statement...........................................    23
    Morgan Reed, President, ACT | The App Association................    27
        Prepared statement...........................................    28

                                    Appendix

    Center for AI Policy, prepared statement.........................    69
    Response to written questions submitted to Ryan Calo by:
        Hon. Maria Cantwell..........................................    71
        Hon. Ben Ray Lujan...........................................    72
        Hon. Ted Cruz................................................    72
    Response to written questions submitted to Amba Kak by:
        Hon. Maria Cantwell..........................................    72
        Hon. Ben Ray Lujan...........................................    73
        Hon. Raphael Warnock.........................................    74
        Hon. Ted Cruz................................................    75
        Hon. Shelley Moore Capito....................................    75
    Response to written questions submitted to Udbhav Tiwari by:
        Hon. Maria Cantwell..........................................    76
        Hon. Ben Ray Lujan...........................................    77
        Hon. Raphael Warnock.........................................    79
        Hon. Ted Cruz................................................    84
        Hon. Shelley Moore Capito....................................    84
    Response to written questions submitted to Morgan Reed by:
        Hon. Ted Cruz................................................    85
        Hon. Shelley Moore Capito....................................    87

          THE NEED TO PROTECT AMERICANS' PRIVACY AND THE AI ACCELERANT

                                  ----------

                            THURSDAY, JULY 11, 2024

                                           U.S. Senate,
            Committee on Commerce, Science, and Transportation,
                                                        Washington, DC.
        The Committee met, pursuant to notice, at 10:04 a.m., in
    room SR-253, Russell Senate Office Building, Hon. Maria
    Cantwell, Chair of the Committee, presiding.
        Present: Senators Cantwell [presiding], Klobuchar, Peters,
    Tester, Rosen, Lujan, Hickenlooper, Welch, Cruz, Thune, Wicker,
    Fischer, Moran, Sullivan, Blackburn, Budd, Schmitt, and Vance.

               OPENING STATEMENT OF HON. MARIA CANTWELL,
                      U.S. SENATOR FROM WASHINGTON

        The Chair. Good morning. The Senate Committee on Commerce,
    Science, and Transportation will come to order.
        I want to thank the witnesses for being here today for
    testimony on the need to protect Americans' privacy and AI as
    an accelerant to the urgency of passing such legislation.
        I want to welcome Dr. Ryan Calo, University of Washington
    School of Law and Co-Director of the University of Washington
    Technology Lab; Ms. Amba Kak, Co-Executive Director of the AI
    Now Institute in New York; Mr. Udbhav Tiwari, Director, Global
    Product Policy for Mozilla, San Francisco; and Mr. Morgan Reed,
    President of ACT | The App Association of Washington, D.C.
        So thank you all for being here for this very, very
    important hearing. We are here today to talk about the need to
    protect Americans' privacy and why AI is an accelerant that
    meets the needs of us passing legislation soon.
        Americans' privacy is under attack. We are being
    surveilled.
        [Feedback in the hearing room.]
        The Chair. Wow, I am being tracked.
        [Laughter.]
        The Chair. We are being surveilled, tracked online in the
    real world through connected devices and now when you add AI it
    is like putting fuel on a campfire in the middle of a
    windstorm.
        For example, a Seattle man's car insurance increased by 21
    percent because his Chevy Volt was collecting detailed
    information about his driving habits and sharing it with data
    brokers who then shared it with his insurance company. The man
    never knew the car was collecting the data.
        Data about our military members including contact
    information and health conditions is already available for sale
    by data brokers for as little as $.12. Researchers at Duke
    University were able to buy such datasets for thousands of our
    active military personnel.
        Every year Americans make millions of calls, texts, chats
    to crisis lines seeking help when they are in mental distress.
    You would expect this information would be kept confidential
    but a nonprofit suicide crisis line was sharing data from those
    conversations with its for-profit affiliates that it was using
    to train its AI product.
        Just this year the FTC sued a mobile app developer for
    tracking consumers' precise location through software it
    embedded in the grocery list and shopping rewards app. The
    company used this data to sort consumers into precise audience
    segments. Consumers whose use of this app helped them remember
    when to buy peanut butter did not expect to be profiled and
    categorized into a precise audience segment like, quote,
    ``parents of preschoolers.''
        These privacy abuses and millions of others that are
    happening every day are bad enough but now AI is an accelerant
    and they are the reason why we need to help in speeding up our
    privacy law.
        AI is built on data, lots of it. Tech companies cannot get
    enough to train their AI models, your shopping habits, your
    favorite videos, who your kids' friends are, all of that, and
    we are going to hear testimony today from Professor Calo about
    this issue, about how AI gives the capacity to derive sensitive
    insights about individuals.
        So it is not just the data that is being collected. It is
    the ability to have sensitive insights about individuals into
    the system. This, as some people have said--you are referring
    to your testimony now--is creating an inference economy that
    could become very challenging.
        That is why I think you also point out, Dr. Calo, that a
    privacy law helps offset the power of these corporations and
    why we need to act. I also want to thank Ms. Kak for her
    testimony because she is clearly talking about that same
    corporate power and the unfair and deceptive practices which we
    have already given to the FTC as their main authority.
        But the lack of transparency about what is going on with
    prompts and the AI synergy is that people are no longer just
    taking personal data and sending us cookie ads.
        They are taking that and putting it actually into prompt
    information. This is a very challenging situation and I think
    your question is, are we going to allow our personal data to
    train AI models is a very important question for our hearing
    today.
        We know that they want this data to feed their AI models to
    make the most amount of money. These incentives are really a
    race to the bottom where most privacy protected companies are
    at a competitive disadvantage.
        Researchers project that if current trends continue
    companies training large language models may run out of new
    publicly available high-quality data to train AI systems as
    early as 2026.
        So without a strong privacy law when the public data runs
    out, nothing stopping it from using our private data, I am very
    concerned that the ability of AI to collect vast amounts of
    personal data about individuals and create inferences about
    them quickly at very low cost can be used in harmful ways like
    charging consumers different prices for the same product.
        I talked to a young developer in my state. I said, what is
    going on, and he said, well, I know one country is using AI to
    basically give it to their businesses. I said, well, why would
    they do that?
        Well, because they want to know when a person calls up for
    a reservation at a restaurant how much income they really have.
    If they do not really have enough money to buy a bottle of wine
    they are giving the reservation to someone else.
        So the notion is that discriminatory practices can already
    exist with just a little amount of data for consumers. AI in
    the wrong hands is also, though, a weapon. Deep fake phone
    scams are already plaguing my state. Scammers used AI to clone
    voices to defraud consumers by posing as a loved one in need of
    money.
        These systems can recreate a person's voice in just
    minutes, taking the familiar grandparent scam and putting it on
    steroids. More alarming, earlier this month the Director of
    National Intelligence reported that Russian influence actors
    are planning to covertly use social media to subvert our
    elections.
        The ODNI called AI, quote, ``A maligned influence
    accelerant,'' end quote, saying that it was being used to more
    convincingly tailor video and other content ahead of the
    November election.
        Just two days ago, the Department of Justice reported that
    it dismantled a Russian bot farm intended to sow discord in the
    United States using AI Russian-created scores of fictitious
    user profiles on X being generated, post, and then use those
    bots to repost ``like'' comments on the post, further
    amplifying the original fake posts.
        So this was possible at tremendous scale, given AI. I am
    not saying that misinformation might have not existed before
    and may not have been placed in a chat group but now with the
    use of bots and AI accelerant that information can be more
    broadly disseminated very, very quickly.
        So privacy is not a partisan issue. According to the Pew
    Research, a majority of Americans across the political spectrum
    want more support for regulation. I believe our most important
    private data should not be bought or sold without our approval
    and tech companies should serve the--and make sure that they
    implement these laws and help stop this kind of interference.
        The legislation that Representative McMorris Rodgers and I
    have worked on I think does just that and I want to say I very
    much appreciate this morning legislation that Senator Blackburn
    and I will be introducing called the COPIED Act, which provide
    much needed transparency around AI-generated content.
        The COPIED Act will also put creators including local
    journalists, artists, and musicians back in control of their
    content with a watermark process that I think is very much
    needed.
        I will now turn to the Ranking Member Senator Cruz for his
    opening statement.

                      STATEMENT OF HON. TED CRUZ,
                        U.S. SENATOR FROM TEXAS

        Senator Cruz. Thank you, Madam Chair.
        American prosperity depends upon entrepreneurs. These are
    ambitious and optimistic men and women who are willing to take
    risks, pursue their dreams, and try to change the world.
        They mortgage their own homes. They put everything on the
    line to build a business that fills an unmet need or does
    something better than what is offered today.
        But throughout history prosperity and human flourishing has
    been stymied or delayed by governments that impose regulatory
    policies to address supposed harms but in actuality overstated
    risk in order to protect incumbent operators, often large and
    powerful companies that did not want to compete and that just
    happened to give big campaign checks to the politicians in
    power.
        The United States has mostly chosen a different path, one
    where a free enterprise system governed by the rule of law
    allows Americans to freely pursue their ideas, grow their own
    businesses, and compete without having to obtain permission
    from all-knowing bureaucrats.
        Today's hearing on data privacy and artificial intelligence
    is a debate about which regulatory path we will take.
        Do we embrace our proven history, one with entrepreneurial
    freedom and technological innovation, or will we adopt the
    European model where government technocrats get to second guess
    and manage perceived risks with economic activity, ultimately
    creating an environment where only big tech with its armies of
    lawyers and lobbyists exist.
        Consider this. In 1993 at the dawn of the tech age the
    economies of the United States and the European Union were,
    roughly, equal in size.
        Today the American economy is nearly 50 percent larger than
    the EU's. The tech boom happened in America in part because
    Congress and the Clinton administration deliberately took a
    hands-off approach to the nascent internet.
        The result was millions of jobs and a much higher standard
    of living for Americans. Unfortunately, the Biden
    administration and many of my colleagues are suggesting the
    European model for AI, based heavily on hysterical doomsday
    prophecies to justify a command and control Federal regulatory
    scheme that will cause the United States to lose our
    technological edge over China.
        The Biden administration's AI executive actions as well as
    many of the AI legislative proposals call for a new regulatory
    order that protects giant incumbent operators and discourages
    innovation with supposedly optional best practices or guidance
    written by all-knowing bureaucrats, some of whom were recently
    employed by the same big tech firms they seek to regulate and
    some of whom hope to be employed again by those same big tech
    firms right after they write the rules that benefit those giant
    big tech firms.
        We already see Federal AI regulators and Biden allies
    talking about the need to stop, quote, ``bias,'' quote,
    ``misinformation,'' quote, ``discrimination'' in AI systems and
    algorithms. That is code for speech police. If they do not like
    what you say they want to silence it.
        Now, AI can certainly be used for nefarious purposes just
    like any other technology, but to address specific harms or
    issues we should craft appropriate and targeted responses.
        For example, Senator Klobuchar and I have introduced the
    bipartisan Take It Down Act which targets bad actors who use AI
    to create and publish fake lifelike explicit images of real
    people.
        Our bill, which is sponsored by many Republican and
    Democrat members of this committee, would also require big tech
    to follow a notice and take down process so ordinary Americans
    who are victimized by these disturbing images can get them
    offline immediately.
        The bipartisan Take It Down Act is a tailored solution to a
    real problem. On behalf of the teenage girls and others who
    have been victimized by deep fake explicit imagery I hope that
    this committee will take up soon the Take It Down Act and pass
    it and move it to the floor and get it signed into law.
        As I conclude, I would like to address a related matter,
    the American Privacy Rights Act--APRA. I support Congress--not
    the FTC or any Federal agency but Congress setting a nationwide
    data privacy standard.
        Not only is it good for Americans to be empowered with
    privacy protections, but it is good for American businesses
    that desperately need legal certainty given the increasingly
    complex patchwork of state laws.
        But our goal should not be to pass any uniform privacy
    standard but rather the right standard that protects privacy
    without preventing U.S. technological innovation.
        I have discussed APRA with Chairwoman McMorris Rodgers and
    will continue my offer to work with her but right now this bill
    is not the solution. It delegates far too much power to
    unelected commissioners at the FTC.
        It focuses on algorithmic regulations under the guise of
    civil rights which would directly empower the DEI speech police
    efforts underway at the Biden White House, harming the free
    speech rights of all Americans.
        As currently constructed APRA is more about Federal
    regulatory control of the Internet than personal privacy. In
    the end, it is the giant companies with vast resources that
    ultimately benefit from bills like APRA at the expense of small
    businesses.
        The path that Congress needs to take is to put individuals
    in control of the privacy of their own data and give them
    transparency to make decisions in the marketplace, and I look
    forward to working with my colleagues to do exactly that.
        The Chair. Thank you, Senator Cruz.
        We will now turn to our panel starting with Dr. Calo. Thank
    you so much. We are really proud of the work that the
    University of Washington has done. I think we are on both.
        Senator Cruz is mentioning the innovation economy. I think
    we have that down in the Northwest, but we also want to make
    sure we have down the important protections that go along with
    it.
        So thank you, Dr. Calo, for your presence.

                  STATEMENT OF RYAN CALO, LANE POWELL

                AND D. WAYNE GITTINGER PROFESSOR OF LAW,

                        UNIVERSITY OF WASHINGTON

        Mr. Calo. Chair Cantwell, Ranking Member Cruz, and members
    of the Committee, thank you for the opportunity to share my
    research and views on this important topic.
        I am a law professor and information scientist at the
    University of Washington where I co-founded the Tech Policy Lab
    and Center for an Informed Public. The views I express today
    are entirely my own.
        Americans are not receiving----
        The Chair. Dr. Calo, could you just bring that microphone a
    little closer to you?
        Mr. Calo. Of course.
        The Chair. Thank you.
        Mr. Calo. Americans are not receiving the privacy
    protections they demand or deserve, not when Cambridge
    Analytica tricked them into revealing personal details of 87
    million people through a poorly vetted Facebook app, not when
    car companies share their driving habits with insurance
    companies without their consent, sometimes leading to higher
    premiums as the senator mentioned.
        Privacy rules are long overdue but the acceleration of
    artificial intelligence in recent years threatens to turn a bad
    situation into a dire one.
        AI exacerbates consumer privacy concerns in at least three
    ways. First, AI fuels an insatiable demand for consumer data.
    Sources of data include what is available online which
    incentivizes companies to scour and scrape every corner of the
    internet, as well as the company's own internal data which
    incentivizes them to collect as much data as possible and store
    it indefinitely. AI's insatiable appetite for data alone deeply
    exacerbates the American consumer privacy crisis.
        Second, AI is increasingly able to derive the intimate from
    the available. Many AI techniques boil down to recognizing
    patterns in large datasets. Even so-called generative AI works
    by guessing the next word, pixel, or sound in order to produce
    new text, art, or music.
        Companies increasingly leverage this capability of AI to
    derive sensitive insights about individual consumers from
    seemingly innocuous information.
        The famous detective Sherlock Holmes, with the power to
    deduce whodunit by observing a string of facts most people
    would overlook as irrelevant, is the stuff of literary fiction
    but companies really can determine who is pregnant based on
    subtle changes to their shopping habits, as Target reportedly
    did in 2012.
        And, finally, AI deepens the asymmetries of information and
    power between consumers and companies that consumer protection
    law exists to arrest.
        The American consumer is a mediated consumer. We
    increasingly work, play, and shop through digital technology,
    and a mediated consumer is a vulnerable one. Our market
    choices, what we see, choose, and click are increasingly
    scripted and arranged in advance.
        Companies have an incentive to use what they know about
    individual and collective psychology plus the power of design
    to extract as much money and attention as they can from
    everyone else.
        The question is not whether America should have rules
    governing privacy. The question is why we still do not. Few
    believe that the internet, social media, or AI are ideal as
    configured.
        A recent survey by the Pew Research Center suggests that an
    astonishing 81 percent of Americans assume that companies will
    use AI in ways for which they are not comfortable--81 percent.
    Just for context, something between 30 and 40 percent of
    Americans identify as Taylor Swift fans.
        Meanwhile, the EU, among our largest trading partners,
    refuses to certify America as adequate on privacy and does not
    allow consumer data to flow freely between our economies.
        What is the point of American innovation if no one trusts
    our inventions? More and more individual states, from
    California to Colorado, Texas to Washington, are passing
    privacy or AI laws to address their residents' concerns.
        Congress can and should look to such laws as a model. Yet,
    it would be unwise to leave privacy legislation entirely to the
    states. The internet, social media, and AI are global
    phenomena.
        They do not respect state boundaries, and the prospect that
    some states will pass privacy rules is small comfort to the
    millions of Americans who reside in states that have not.
        Congress should pass comprehensive privacy legislation that
    protects American consumers, reassures our trading partners,
    and gives clear achievable guidelines to industry.
        Data minimization rules which obligate companies to limit
    the data they collect and maintain about consumers could help
    address AI's insatiable appetites.
        Broader definitions of covered data could clarify that
    inferring sensitive information about consumers carries the
    same obligations as collecting it and rules against data misuse
    could help address consumer vulnerability in the face of a
    growing asymmetry.
        Thank you for the opportunity to testify before the
    Committee. I look forward to a robust discussion.
        [The prepared statement of Mr. Calo follows:]

      Prepared Statement of Ryan Calo, Lane Powell and D. Wayne Gittinger
                   Professor of Law, University of Washington
        Chairwoman Cantwell, Ranking Member Cruz, and Members of the
    Committee, thank you for the opportunity to share my research and views
    on the important issue of artificial intelligence (AI) and privacy.
        I am the Lane Powell and D. Wayne Gittinger Professor of Law at the
    University of Washington where I hold appointments at the Information
    School and, by courtesy, the Paul G. Allen School of Computer Science
    and Engineering. I have written dozens of articles on AI, privacy, and
    their interaction. Together with colleagues, I founded the
    interdisciplinary Tech Policy Lab and Center for an Informed Public. I
    am a board member of the R Street Institute and serve as a privacy
    judge for the World Bank. I occasionally advise companies on technology
    policy and ethics and am of counsel to the law firm Wade, Kilpela, &
    Slade LLP. Prior to academia, I worked as a privacy law associate in
    the D.C. office of Covington & Burling LLP. The views I express in this
    testimony are my own.
        Americans are not receiving the privacy protections they demand or
    deserve. Chicago resident Mike Seay did not receive the privacy
    protections his family deserves when, in 2014, OfficeMax sent him a
    marketing letter addressed to ``Mike Seay, Daughter Killed in a Car
    Crash.'' \1\ Facebook users did not get the privacy protections they
    deserve when Cambridge Analytica tricked them into revealing personal
    details of 87 million people through a poorly vetted Facebook app.\2\
    And General Motors consumers did not get the privacy protections they
    deserve when their driving habits were sold to insurance companies
    without consent, sometimes leading to higher premiums.\3\
    ---------------------------------------------------------------------------
        \1\ Nesita Kwan, OfficeMax Sends Letter to ``Daughter Killed in Car
    Crash,'' NBC News (January 19, 2014), online at https://
    www.nbcchicago.com/news/national-international/officemax-sends-letter-
    to-daughter-killed-in-car-crash/1986493/.
        \2\ Deepa Seetharaman & Katherine Bindley, Facebook Controversy:
    What to Know about Cambridge Analytica and Your Data, Wall Street
    Journal (March 23, 2018), online at https://www.wsj.com/articles/
    facebook-scandal-what-to-know-about-cambridge-analytica-and-your-data-
    1521806400.
        \3\ Kashmir Hill, Automakers Are Sharing Consumers' Driving
    Behavior With Insurance Companies, New York Times (March 11, 2024),
    online at https://www.nytimes.com/2024/03/11/technology/carmakers-
    driver-tracking-insurance.html.
    ---------------------------------------------------------------------------
        Privacy rules are long overdue. But the acceleration of AI over the
    past few years threatens to turn a bad situation into a dire one.
        AI exacerbates consumer privacy concerns in at least three ways.
    First, AI fuels an insatiable demand for consumer data. Second, AI
    allows companies and governments to derive intimate details about
    people from widely available information. And third, AI renders
    consumers more vulnerable to commercial exploitation by deepening the
    asymmetries of information and power between consumers and companies
    that consumer protection law exists to address. American society can no
    longer afford to sacrifice consumer privacy on the altar of innovation,
    nor leave the task of protecting Americans' privacy to a handful of
    individual states.
        AI fuels an insatiable demand for consumer data. AI is best
    understood as a set of techniques aimed at approximating some aspect of
    human or animal cognition using machines.\4\ As I told Wired Magazine
    in a 2021 story about the dangers of facial recognition technology, AI
    is like Soylent Green: it's made out of people.\5\ AI as deployed today
    requires an immense amount of data by and about people to train its
    models. Sources of data include what is available online, which
    incentivizes companies to scour and scrape every corner of the
    internet,\6\ as well as the company's own internal data, which
    incentivizes them to collect as much data on consumers as possible and
    store it indefinitely. AI's insatiable appetite for data alone
    exacerbates the American consumer privacy crisis.
    ---------------------------------------------------------------------------
        \4\ Ryan Calo, Artificial Intelligence Policy: A Primer and
    Roadmap, 51 UC Davis Law Review 399 (2017).
        \5\ Tom Simonite, A Startup Will Nix Algorithms Built on Ill-Gotten
    Facial Data, Wired (January 12, 2021), online at https://www.wired.com/
    story/startup-nix-algorithms-ill-gotten-facial-data/.
        \6\ Daniel J. Solove & Woodrow Hartzog, The Great Scrape: The Clash
    Between Scraping and Privacy, online at https://papers.ssrn.com/sol3/
    papers.cfm?abstract_id=4884485.
    ---------------------------------------------------------------------------
        AI is increasingly able to derive the intimate from the available.
    Many AI techniques boil down to recognizing patterns in large data
    sets. Even so-called generative AI works by guessing the next word,
    pixel, or sound in order to produce new text, art, or music. Companies
    are increasingly able to use this capability to derive sensitive
    insights about individual consumers from public or seemingly innocuous
    information. The famous detective Sherlock Holmes--with the power to
    deduce whodunit by observing a string of facts most people would
    overlook as irrelevant--is the stuff of literary fiction. But companies
    really can determine who is pregnant based on subtle changes to their
    shopping habits, as Target did in 2012,\7\ or diagnose postpartum
    depression with 83 percent accuracy based on parent Twitter
    activity.\8\
    ---------------------------------------------------------------------------
        \7\ Charles Duhigg, How Companies Learn Your Secrets, New York
    Times (February 16, 2012), online at https://www.nytimes.com/2012/02/
    19/magazine/shopping-habits.html The rising threat to sexual privacy
    seems especially acute, as Danielle Keats Citron presciently argues.
    Danielle Keats Citron, The Fight for Privacy: Protecting Dignity,
    Identity, and Love in the Digital Age (WW Norton 2023).
        \8\ Munmun De Choudhury, Scott Counts, & Eric Horvitz, Predicting
    Postpartum Changes in Emotion and Behavior via Social Media, CHI 2013,
    online at https://www.microsoft.com/en-us/research/publication/
    predicting-postpartum-changes-emotion-behavior-via-social-media/.
    ---------------------------------------------------------------------------
        The ability of AI to derive sensitive information such as pregnancy
    or mental health based on seemingly non-sensitive information creates a
    serious gap in privacy protection. Many laws draw a distinction between
    personal and non-personal, public and private, sensitive and non-
    sensitive data--protecting the former but not the latter. AI breaks
    down this distinction, leaving everyone more vulnerable. ``Contemporary
    information privacy protections do not grapple with the way that
    machine learning facilitates an inference economy'' writes law
    professor Alicia Solow-Niederman ``in which organizations use available
    data collected from individuals to generate further information about
    both those individuals and about other people.'' \9\
    ---------------------------------------------------------------------------
        \9\ Alicia Solow-Niederman, Information Privacy and the Inference
    Economy, 117 Northwestern University Law Review 357 (2022).
    ---------------------------------------------------------------------------
        AI deepens the asymmetries of power between consumers and companies
    that consumer protection law exists to address. Most of us think of
    accomplishing tasks with technology, such as a calculator or cash
    register. Increasingly, however, Americans work, play, and purchase
    through technology. The American consumer is mediated by computer code,
    and a mediated consumer is a vulnerable one. Our market choices--what
    we see, choose, and click--are scripted and arranged in advance. As I
    and other privacy scholars show through a series of law review
    articles, modern companies study and design every aspect of their
    interactions with consumers.\10\ Companies employ people with letters
    after their names to study how to extract as much money and attention
    as possible from the user. They then design their online store, mobile
    game, or social media platform accordingly. Companies have an incentive
    to use what they know about people plus the power of design to extract
    social surplus from everyone else. And they do.
    ---------------------------------------------------------------------------
        \10\ E.g., Ryan Calo, Digital Market Manipulation, 82 George
    Washington Law Review 995 (2014).
    ---------------------------------------------------------------------------
        Sometimes the design choices of companies are so egregious that the
    Federal Trade Commission has pursued them as deceptive (aka ``dark'')
    patterns. A recent FTC complaint alleges, for instance, that Amazon
    tricked consumers into enrolling in Amazon Prime through the
    manipulation of defaults.\11\ Such tactics are especially problematic
    when they combine a general understanding of consumer psychology with
    specific knowledge about individual consumer vulnerabilities. For
    example, the ridesharing platform Uber once studied whether people
    might be more willing to pay for surge pricing if the battery on their
    phone was running out.\12\
    ---------------------------------------------------------------------------
        \11\ FTC Takes Action Against Amazon for Enrolling Consumers in
    Amazon Prime Without Consent and Sabotaging Their Attempts to Cancel,
    Federal Trade Commission (June 21, 2013), online at https://
    www.ftc.gov/news-events/news/press-releases/2023/06/ftc-takes-action-
    against-amazon-enrolling-consumers-amazon-prime-without-consent-
    sabotaging-their.
        \12\ Ryan Calo & Alex Rosenblat, The Taking Economy: Uber,
    Information, and Power, 117 Columbia Law Review 1623 (2017).
    ---------------------------------------------------------------------------
        AI dials the extractive potential of ``informational capitalism''
    \13\ up to 11. Companies use AI to derive orders of magnitude more
    knowledge about consumers, building it into our experiences in real-
    time. Rather than everything costing $9.99 because it feels farther
    than a cent away from $10, everything will cost the most the consumer
    is willing to pay in the moment--what economists call our ``reservation
    price.'' \14\ Luke Stark and Jevan Hutson use the term ``physiognomic
    AI'' to refer to the practice of using machine learning to infer
    identities, social status, and future social outcomes based on the
    physical, emotional, or behavioral characteristics of consumers.\15\
    Such techniques are also being deployed in a variety of contexts,
    including ``optimizing'' worker productivity, teaching and learning,
    and on-and offline marketing.
    ---------------------------------------------------------------------------
        \13\ Julie Cohen, Between Truth and Power: The Legal Constructions
    of Informational Capitalism (Oxford University Press 2019).
        \14\ For examples, see Albert Fox Cahn, AI is quietly being used to
    pick your pocket, Business Insider (June 9, 2024), online at https://
    www.businessinsider.com/ai-quietly-picking-your-pocket-with-
    personalized-pricing-2024-7.
        \15\ Luke Stark and Jevan Hutson, Physiognomic Artificial
    Intelligence, 32 Fordham Intellectual Property Media and Entertainment
    Law Journal 922 (2022).
    ---------------------------------------------------------------------------
        The future of AI is more concerning still. The increasing ability
    of AI to mimic people, for example, generates myriad new opportunities
    for consumer harm.\16\ As study after study shows, people are hardwired
    to react to anthropomorphic technology like AI as though it is really
    social.\17\ Thousands of people are turning to AI-powered
    ``therapists,'' creating a record of their most intimate thoughts and
    behaviors with few privacy safeguards.\18\ Companies such as Replika--
    the ``AI companion who cares''--have even sought to monetize this human
    tendency to anthropomorphize by charging consumers more to enter into
    romantic relationships with the company's bots.\19\ The AI literally
    flirts with consumers to try to get them to switch to premium.\20\
    ---------------------------------------------------------------------------
        \16\ Ian Kerr, Bots, Babes and the Californication of Commerce, 1
    University of Ottawa Law and Technology Journal 285 (2004); Woodrow
    Hartzog, Unfair and Deceptive Robots, 74 Maryland Law Review 786
    (2015).
        \17\ Ryan Calo, Robotics and the Lessons of Cyberlaw, 103
    California Law Review 513 (2015).
        \18\ Simon Coghlan, Kobi Leins, Susie Sheldrick, Marc Cheong, Piers
    Gooding, Simon D'Alfosno, To chat or not to chat: Ethical Issues with
    using chatbots in mental health, Digit Health (June 22, 2023), online
    at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10291862/.
        \19\ Daniella DiPoala & Ryan Calo, Socio-Digital Vulnerability,
    online at https://papers.ssrn
    .com/sol3/papers.cfm?abstract_id=4686874.
        \20\ Id. The example comes from Boine, Claire. 2023. ``Emotional
    Attachment to AI Companions and European Law.'' MIT Case Studies in
    Social and Ethical Responsibilities of Computing, no. Winter 2023
    (February). https://doi.org/10.21428/2c646de5.db67ec7f.
    ---------------------------------------------------------------------------
        Ultimately the purpose of privacy and other consumer protection law
    is to offset such aggregations of corporate power. As Professor Robert
    Lande shows through a detailed analysis of the legislative records of
    the Sherman Act, the FTC Act, and other turn of the century consumer
    protection laws, ``Congress was concerned principally with preventing
    `unfair' transfers of wealth from consumers to firms with market
    power.'' \21\ This is why Section V of the FTC Act instructs the
    Commission to pursue ``unfair'' and deceptive practice. Substitute the
    term ``AI'' for ``market power'' and Congress' responsibility is clear:
    consumers need their government to help offset the immense asymmetries
    of information and power that AI provides the companies who deploy it.
    ---------------------------------------------------------------------------
        \21\ Robert H. Lande, Wealth Transfer as the Original and Primary
    Concern of Antitrust: The Efficiency Interpretation Challenged, 34
    Hastings Law Journal 65 (1982).
    ---------------------------------------------------------------------------
        Federal consumer privacy legislation is long overdue. The question
    is not whether America should have rules governing privacy. The
    question is why we still do not. Few believe that the internet, social
    media, or AI are ideal as configured. Industry's relentless pursuit of
    consumer data has undermined privacy, fueled misinformation,\22\ and is
    harming the environment.\23\ Existing safeguards are deeply
    inadequate.\24\
    ---------------------------------------------------------------------------
        \22\ Renee DiResta, The Supply of Disinformation Will Soon Be
    Infinite, The Atlantic (September 20, 2020), online at https://
    www.theatlantic.com/ideas/archive/2020/09/future-propaganda-will-be-
    computer-generated/616400/.
        \23\ Clare Duffy, Google's greenhouse gas emissions are soaring
    thanks to AI, CNN (July 3, 2024), online at https://www.cnn.com/2024/
    07/03/tech/google-ai-greenhouse-gas-emissions-environmental-impact/
    index.html.
        \24\ Ari Ezra Waldman, Industry Unbound: The Inside Story of
    Privacy, Data, and Corporate Power (Cambridge University Press 2021).
    ---------------------------------------------------------------------------
        There is a lingering concern that privacy rules will hamper
    innovation. The opposite is true. Today's absence of privacy rules is
    actively undermining consumer trust.\25\ Just as spam threatened to
    make e-mail unusable until Congress passed the CAN-SPAM Act, so has the
    unfettered collection, processing, use, and sharing of data led to a
    crisis of consumer confidence. Recent research by Pew suggests that an
    astonishing eighty-one percent of Americans assume AI companies will
    use their information in ways with which they are not comfortable.\26\
    Meanwhile the EU, among our largest trading partners, refuses to
    certify America as ``adequate'' on privacy and does not allow consumer
    data to flow freely between our economies. What is the point of
    American innovation if no one trusts our inventions?
    ---------------------------------------------------------------------------
        \25\ Neil Richards, Why Privacy Matters (Oxford University Press
    2021).
        \26\ Colleen McClain, Michelle Faverio, Monica Anderson, & Eugene
    Park, How Americans View Data Privacy, Pew Research Center (October 18,
    2023), online at https://www.pewresearch.org/short-reads/2023/10/18/
    key-findings-about-americans-and-data-privacy/.
    ---------------------------------------------------------------------------
        Individual states such as Illinois, California, and Washington have
    responded to consumer harms and mistrust by passing privacy rules of
    their own.\27\ Congress can and should look to such laws as a model.
    Yet it would be unwise to leave privacy legislation entirely to the
    states. The internet, social media, and AI are global phenomena; they
    do not respect state borders. Regulating a distributed industry is
    quintessentially the province of the Federal government (and the reason
    for the Commerce Clause in the Constitution). Expecting tech companies
    to comply with a patchwork of laws depending on what state a consumer
    happens to access their services is unrealistic and wasteful. And the
    prospect that some states will pass privacy rules is small comfort to
    the millions upon millions of Americans who reside in states that have
    not.
    ---------------------------------------------------------------------------
        \27\ The Illinois Biometric Information Privacy Act of 2008; the
    California Privacy Protection Act of 2018, as amended by the California
    Privacy Rights Act; the Washington My Health Data Act of 2023.
    ---------------------------------------------------------------------------
        Congress should pass comprehensive privacy legislation that
    protects American consumers, reassures our trading partners, and gives
    clear, achievable guidelines to industry. Data minimization rules--
    which obligate companies to limit the data they collected and maintain
    about consumers--could help address AI's insatiable appetites. Broader
    definitions of covered data could clarify that inferring sensitive
    information about consumers carries the same obligations as collecting
    it. And rules again data misuse or abuse could help address consumer
    vulnerability in the face of growing asymmetry. Congress has the power
    to deliver innovation Americans and the world can start to trust.
        Congress should also look toward the future. Passing comprehensive
    privacy legislation is necessary today. But technology will not stand
    still. My parting recommendation is for Congress to start to prepare
    now for the next wave of innovation. In particular, Congress should
    reestablish the Office of Technology Assessment (OTA). For twenty
    years, the OTA helped Congress anticipate and understand emerging
    technologies and make wiser decisions around them. Hearings are
    important, but there is no substitute for a dedicated,
    interdisciplinary, bipartisan staff. Congress should also adequately
    fund other expert bodies--especially the National Institute of
    Standards and Technology. Only by ensuring that Congress has access to
    deep and impartial technical expertise can America hope to anticipate
    future disruption.
        Thank you for this opportunity to testify before the Committee. I
    look forward to a robust discussion.

        The Chair. Thank you, Dr. Calo.
        Ms. Kak, thank you so much. Welcome. We look forward to
    your testimony.

             STATEMENT OF AMBA KAK, CO-EXECUTIVE DIRECTOR,
                            AI NOW INSTITUTE

        Ms. Kak. Thank you, Chair Cantwell, Ranking Member Cruz,
    and esteemed members of this committee. Thank you for inviting
    me to testify.
        We are at a very clear inflection point in the trajectory
    of AI. Without guardrails to set the rules of the road we are
    committing ourselves to more of the same, to carrying forward
    extractive, invasive, and often predatory data practices and
    business models that have characterized the past decade of the
    tech industry.
        We are also committing ourselves to the seamless transition
    of big tech from surveillance monopolies to AI monopolies. A
    Federal data privacy law could break this cycle, especially one
    with strong data minimization, which could challenge the
    culture of impunity and recklessness in the AI industry that is
    already hurting both consumers and competition.
        If there is a single point I want to make in today's
    testimony it is that now is the moment when passing such a law
    actually matters before the trajectory has been set.
        Data privacy regulation is AI regulation and it provides
    many of the tools that we need to protect the public from harm.
    But let us get concrete. How might the AI market shape up
    differently in the presence of a strong data privacy law?
        First, with data minimization rules firms would need to put
    reason in place of recklessness when it comes to decisions
    about what data to collect, the purposes to which it can be
    used, and for how long it can be stored.
        These requirements would empower lawmakers but also the
    public to demand very basic accountability. So before
    Microsoft, for example, rushes to release its new Recall.ai
    feature, which, by the way, takes continuous screenshots of
    everything you see or do on your computer, the company might
    need to ask itself and, importantly, document its answer does
    the utility of this feature outweigh the honeypot it is
    creating for bad actors?
        As a security researcher quickly discovered with Microsoft
    Recall it is actually scarily trivial for an attacker to use
    malware to extract a record of everything you have ever viewed
    on your PC.
        Now, a strong data minimization mandate would have nipped
    this in the bud. It would have likely disincentivized the
    development of such a patently insecure feature to begin with.
        Second, we would have transparency about the data decisions
    that affect us all. Meta and Google recently announced updates
    unilaterally, of course, to their terms which allow AI training
    from user data.
        Now, we know about this because European users were alerted
    by Meta. Without a legal mandate to require it, of course,
    American users received no such notification and users in--of
    Reddit are also similarly up in arms because their content was
    just sold to the highest bidder, in this case Google, for use
    of training its AI.
        But it is important to remember that a privacy law would
    offer much more than just transparency in every single instant
    I mentioned. Purpose limitation rules would prevent big tech
    from using AI as its catch-all justification to use and combine
    data across contexts and store it forever.
        The FTC has already penalized Amazon for storing the voice
    data of children indefinitely, using AI as its excuse. But we
    cannot rely on this kind of one-off enforcement. There needs to
    be rules of the road.
        And these rules, it is important to remember, would not
    just safeguard our privacy. They would also act as a powerful
    check on the data advantages currently being consolidated by
    big tech to build a moat around them and stave off competition
    in AI.
        Third, in a world with a data privacy mandate AI developers
    would need to make data choices that deliberately prevent
    discriminatory outcomes. So we should not be surprised when we
    see that women are seeing far less ads for high-paying jobs in
    Google ads. That is 100 percent a feature of data decisions
    that have already been made upstream.
        I mean, the good news here is that these are avoidable
    problems, and it is not just in scope for privacy law. I would
    say it is integral to protecting people from the most serious
    abusers of this data and where specific AI practices have sort
    of inherent, well-established harms, emotion recognition
    systems that have faulty scientific foundations or pernicious
    forms of ad targeting. The law would hold them entirely off
    limits.
        Finally--and here is the thing about very large-scale AI--
    it is not only computationally, ecologically, and data
    intensive it is also very, very expensive to develop and run.
        Now, these eye-watering costs will need a path to profit
    and by every account a viable business model still does not
    exist. Now, it is precisely in this kind of an environment with
    a few incumbent firms feeling the pressure to turn a profit
    that predatory business models emerge.
        Meanwhile, we are hearing new research that LLM systems are
    capable of hyper personalized inferences about us even from the
    most general prompts. You do not need to be a clairvoyant to
    see that all roads might well be leading us right back to the
    surveillance advertising business model that got us here.
        So, to conclude, there is nothing about the current
    trajectory of AI that is inevitable and as a democracy the U.S.
    has a huge opportunity to take global leadership and shape this
    next era of tech so that it reflects the public interest and
    not just the bottom lines of very few companies.
        This is the moment for action. Thank you.
        [The prepared statement of Ms. Kak follows:]

    Prepared Statement of Amba Kak, Co-Executive Director, AI Now Institute
     on behalf of herself and Dr. Sarah Myers West, Co-Executive Director,
                                AI Now Institute
        Chair Cantwell, Ranking Member Cruz, and esteemed Members of the
    Committee, thank you for inviting me to testify on this important set
    of issues. I deeply appreciate this Committee for taking the initiative
    to spotlight this urgently needed conversation, and in particular for
    recognizing that privacy and AI innovation are mutually reinforcing
    goals that can, and must, be advanced in concert. My name is Amba Kak,
    and I co-lead the AI Now Institute, a leading policy research institute
    founded in 2016 that focuses on the social and economic impacts of
    artificial intelligence technologies. I have spent over fifteen years
    as a global policy expert designing and advocating for technology
    policy in the public interest, examining topics ranging from privacy to
    competition to algorithmic accountability, across roles in government,
    industry, and civil society. I recently served as a senior advisor on
    artificial intelligence at the Federal Trade Commission, where my role
    was to provide technological expertise in support of the agency's
    enforcement and policy work, focused on how to mitigate and redress
    harms from data-driven systems like AI. This testimony is offered on
    behalf of myself and my colleague Dr. Sarah Myers West, and our remarks
    are based on research we have conducted at AI Now.\1\
    ---------------------------------------------------------------------------
        \1\ See generally Amba Kak and Sarah Myers West, ``AI Now 2023
    Landscape: Confronting Tech Power,'' AI Now Institute, April 11, 2023,
    https://ainowinstitute.org/2023-landscape.
    ---------------------------------------------------------------------------
        As excitement and trepidation about large-scale AI systems
    continues to fill headlines and hearings, it's important to remember
    that nothing about the current trajectory of these privately developed
    technologies is inevitable. In a democracy, the trajectory of powerful
    technologies should be shaped in the public interest through public
    deliberation, not solely by a handful of corporate actors driven,
    ultimately, by commercial incentives: regulation can play a crucial
    role in ensuring such democratic shaping of technological systems.
        Which brings me to the one overarching point I want to make in
    today's testimony: the trajectory of AI is at a crucial inflection
    point. Without regulatory intervention, we are doomed to replicate the
    extractive, invasive, and often harmful data practices and business
    models that have characterized the past decade of the tech industry. A
    Federal data privacy law, especially one with strong data minimization,
    could act as a foundational intervention to break this cycle and
    challenge the culture of impunity and recklessness that is hurting both
    consumers and competition.
        In fact, the notion that we need to wipe away years of regulation
    and policy and create new frameworks from scratch for AI serves large
    industry players more than it does the rest of us: it serves to delay,
    and to provide current actors with significant influence on the scope
    and direction of such policymaking. AI systems are not wholly novel.
    Far from it. And rather than view them that way, to responsibly govern
    these technologies we must instead disaggregate these systems, or the
    ``AI stack,'' into their composite inputs, recognizing the details of
    how they work and what they require to operate. These include close
    examination of data, computational infrastructure, and labor. Precise
    and technically aware regulatory strategies can then be deployed at
    different layers of this stack, preventing cloud companies from using
    their dominant market position to restrict competition in the AI
    market, for example; or copyright strategies against use of artistic
    works by image-generation tools; or, as is the subject of this
    testimony, AI firms from the irresponsible collection and retention of
    personal information.\2\ Once this is done, we can explore whether new
    approaches to address previously unanticipated harms or to tackle
    specific sectoral use cases are needed. Before that, though, we must
    leverage and continue to strengthen the regulatory toolbox we have
    already honed over the past decade.
    ---------------------------------------------------------------------------
        \2\ See Jai Vipra and Sarah Myers West, ``Computational Power and
    AI,'' AI Now Institute, September 27, 2023, https://ainowinstitute.org/
    publication/policy/compute-and-ai; Tejas Narechania and Ganesh
    Sitaraman, ``An Antimonopoly Approach to Governing Artificial
    Intelligence,'' Vanderbilt Policy Accelerator for Political Economy and
    Regulation, Vanderbilt University, October 6, 2023, https://
    cdn.vanderbilt.edu/vu-URL/wp-content/uploads/sites/412/2023/10/
    06212048/Narechania-Sitaraman-Antimonopoly-AI-2023.10.6.pdf.pdf; and
    Jennifer Cobbe, Michael Veale, and Jatinder Singh, ``Understanding
    Accountability in Algorithmic Supply Chains,'' 2023 ACM Conference on
    Fairness, Accountability, and Transparency (FAccT '23), April 7, 2023,
    https://ssrn.com/abstract=4430778.
    ---------------------------------------------------------------------------
        To illuminate my argument, I will divide it into three specific
    points:

        First, privacy risks are implicated across the AI life cycle. The
    generative AI boom further unleashes new forms of familiar privacy
    harms, supercharges the incentives for irresponsible data surveillance,
    and creates conditions ripe for extractive and exploitative business
    models.

        Second, the turn toward large-scale AI further consolidates Big
    Tech's already staggering control over consumer data, which deepens
    power asymmetries and allows these companies to act recklessly and with
    impunity. A strong data minimization rule would ensure not only the
    advancement of privacy, but would also act as a powerful curb on the
    concentration of power we've seen in this sector.

        Finally, a legally binding data privacy mandate, including strong
    data minimization, individual data rights, algorithmic impact
    assessments, and protections against algorithmic discrimination, offers
    a foundational toolkit for demanding accountability from AI companies.
        I. Privacy risks are implicated across the AI life cycle. The
    generative AI boom further unleashes new forms of familiar privacy
    harms, supercharges the incentives for irresponsible data surveillance,
    and creates conditions ripe for extractive and exploitative business
    models.
        In the wake of a highly charged AI race with companies rushing to
    release new products and features to market before competitors, we're
    seeing a sharp uptick in privacy lapses, from unexpected leaks of
    personal information in chatbot outputs\3\ to features that threaten to
    fundamentally compromise the privacy and security of our personal
    devices.\4\ While the list of egregious and obvious privacy failures is
    already long, we also need a systematic approach that brings into view
    every stage of the AI data life cycle as well as captures the more
    structural pathologies set in motion by the AI boom.
    ---------------------------------------------------------------------------
        \3\ Jordan Pearson, ``ChatGPT Can Reveal Personal Information from
    Real People, Google Researchers Show,'' Vice, November 29, 2023,
    www.vice.com/en/article/88xe75/chatgpt-can-reveal-personal-information-
    from-real-people-google-researchers-show.
        \4\ Zak Doffman, ``Google Confirms Serious AI Risks for iPhone and
    Android Users,'' Forbes, February 15, 2024, www.forbes.com/sites/
    zakdoffman/2024/02/12/google-warns-as-free-ai-upgrade-for-iphone-
    android-and-samsung-users.
    ---------------------------------------------------------------------------
        In this vein, we look into privacy harms that (1) emanate at the
    training and development stage, (2) emanate at the application and
    output stage, and those that (3) emanate from the business models and
    incentives shaping the AI market.
        (I) Training and development stage. AI models are trained on large
    amounts of data, and the early stages of training and then fine-tuning
    models can set in motion some of the most harmful and far-reaching data
    practices.
        While there is a lack of basic transparency about the datasets used
    to train many commercially available models today, we know that at
    least some have taken advantage of publicly available data, scraping
    the web to create massive datasets of images and text, as well as voice
    and video data. In 2009, Meta changed its settings so that much
    previously private user data became public by default while users
    scrambled to revert to their original settings.\5\ A month later, Mark
    Zuckerberg disingenuously argued that privacy was no longer the
    ``social norm.'' \6\ Statements like this subvert the most basic
    privacy expectations of citizens whose digital lives are hoovered up by
    firms for profit, shielded by broad and inscrutable terms of service
    and settings that can be changed without people's consent. Only through
    public scandals like Cambridge Analytica in 2018 did the public become
    aware that it had to contend with the dangers of this kind of
    centralized data power in the hands of a few companies.\7\
    ---------------------------------------------------------------------------
        \5\ Nick Bilton, `` `He Doesn't Believe in It': Mark Zuckerberg Has
    Never Cared about Your Privacy, and He's Not Going to Change,'' Vanity
    Fair, November 20, 2018, https://www.vanityfair.com/news/2018/11/mark-
    zuckerberg-has-never-cared-about-your-privacy.
        \6\ Bobbie Johnson, ``Privacy No Longer a Social Norm, Says
    Facebook Founder,'' Guardian, January 10, 2010, https://
    www.theguardian.com/technology/2010/jan/11/facebook-privacy.
        \7\ Ibid.
    ---------------------------------------------------------------------------
        Developments in generative AI have brought into sharp focus the
    stakes of this free-for-all approach to mining the public sphere. Soon
    after the public release of ChatGPT, questions from the public about
    what data these AI models had been trained on began to circulate,\8\
    followed by panic when people began to realize that ChatGPT was
    sometimes leaking personal data ``accidentally'' in response to
    prompts.\9\
    ---------------------------------------------------------------------------
        \8\ Clothilde Goujard, ``Italian Privacy Regulator Bans ChatGPT,''
    Politico, March 31, 2023, https://www.politico.eu/article/italian-
    privacy-regulator-bans-chatgpt.
        \9\ See Nicholas Carlini et al., ``Extracting Training Data from
    Large Language Models,'' 30th USENIX Security Symposium, December 2020,
    https://arxiv.org/abs/2012.07805; Nicholas Carlini et al., ``Extracting
    Training Data from Diffusion Models,'' January 2023, https://arxiv.org/
    abs/2301.13188; and OpenAI, ``March 20 ChatGPT Outage: Here's What
    Happened,'' March 24, 2023, https://openai.com/blog/march-20-chatgpt-
    outage.
    ---------------------------------------------------------------------------
        We're also seeing Big Tech firms store and use data collected in
    one context for other unanticipated purposes, using AI as a catchall
    justification. Companies haven't given clear answers to the question of
    whether or not they're using internal data to train new AI models,\10\
    and Meta and Google recently announced an update to their terms that
    explicitly allows training of AI from user data.\11\ These fundamental
    choices of what data to use to train AI models determine the likelihood
    of inaccurate and discriminatory outputs. Recent research demonstrates
    that as AI models scale, using larger and larger datasets like the ones
    used in current LLMs, the tendency to produce inaccurate and harmful
    stereotypes also scales.\12\ Meanwhile, a decade of evidence on
    predictive AI systems now bears out the ``garbage in, garbage-out''
    thesis, which holds that inaccurate, incomplete, and discriminatory
    training datasets go on to produce decisions or recommendations in
    high-stakes domains with harmful consequences for people's lives.\13\
    ---------------------------------------------------------------------------
        \10\ Cordilia James, ``Are Instagram and Facebook Really Using Your
    Posts to Train AI? What to Know,'' Wall Street Journal, June 21, 2024,
    https://www.wsj.com/tech/ai/meta-ai-training-instagram-facebook-
    explained-a3d36cdb.
        \11\ Eli Tan, ``When the Terms of Service Change to Make Way for
    A.I. Training,'' New York Times, June 26, 2024, https://
    www.nytimes.com/2024/06/26/technology/terms-service-ai-training.html.
        \12\ Abeba Birhane et al, ``On Hate Scaling Laws For Data-Swamps'',
    June 28 2023, https://arxiv.org/abs/2306.13141.
        \13\ See Heather Rodroguez, ``Garbage In, Garbage Out: The
    Potential Pitfalls of Artificial Intelligence,'' Texas A&M University
    College of Arts and Sciences, January 19, 2023, artsci
    .tamu.edu/news/2023/01/garbage-in-garbage-out-the-potential-pitfalls-
    of-artificial-intelligence
    .html; and Joan M. Teno, ``Garbage In, Garbage Out--Words of Caution on
    Big Data and Machine Learning in Medical Practice,'' JAMA Forum,
    February 16, 2023, https://jamanet
    work.com/journals/jama-health-forum/fullarticle/2801776.
    ---------------------------------------------------------------------------
        (2) Applications, outputs, and decision-making stage. Downstream,
    we see a new range of privacy threats culminate as AI models are
    applied to consumer-facing applications, or used in systems that aid or
    make decisions, recommendations, or inferences that impact people's
    lives in material ways.
        Generative AI systems currently on the market have been
    unexpectedly and routinely leaking personal information that is traced
    back to training datasets, including sensitive or even confidential
    data.\14\ While generative AI companies advise their users not to
    include personal information in their prompts, many still do;\15\ more
    concerningly, research suggests that LLM-powered systems like ChatGPT
    are capable of making detailed and sensitive inferences even from
    apparently anonymized prompts.\16\ Mindful of these unresolved and
    persistent privacy challenges, many of the largest technology firms
    have banned their employees from using services like ChatGPT.
    ---------------------------------------------------------------------------
        \14\ Lily Hay Newman, ``ChatGPT Spit Out Sensitive Data When Told
    to Repeat `Poem' Forever,'' Wired, December 2, 2023, https://
    www.wired.com/story/chatgpt-poem-forever-security-roundup.
        \15\ Heidi Mitchell, ``Is It Safe to Share Personal Information
    With a Chatbot?'' Wall Street Journal, January 18, 2024, https://
    www.wsj.com/tech/ai/ai-chatbot-sharing-personal-information-229d41a0.
        \16\ Mack DeGeurin, ``ChatGPT Can `Infer' Personal Details from
    Anonymous Text,'' Gizmodo, October 17, 2023, gizmodo.com/chatgpt-llm-
    infers-identifying-traits-in-anonymous-text-185093
    4318.
    ---------------------------------------------------------------------------
        With a scramble to rush to market and a lack of regulatory
    friction, we're seeing multiple AI companies announce untested and
    potentially harmful applications of AI that rely on people's sensitive
    information--including biometrics--to make questionable inferences. For
    example, on the occasion of OpenAI's recent rollout of Sora, a chatbot
    that provides multimedia output in response to prompts, CEO Sam Altman
    claimed that the software could detect emotional states from people's
    voice recordings--even as there is mounting evidence (acknowledged by
    regulators globally\17\) that such inferences have dubious scientific
    validity, and potentially reinforce inaccurate and discriminatory
    stereotypes. Data privacy laws around the world are already being used
    to put in place strict limitations on specific kinds of data use that
    have well-known harms, including such ``emotion recognition''
    systems\18\ as well as targeted advertising to children.\19\
    ---------------------------------------------------------------------------
        \17\ Information Commissioner's Office, `` `Immature Biometric
    Technologies Could Be Discriminating against People' Says ICO in
    Warning to Organisations,'' October 26, 2022, https://ico
    .org.uk/about-the-ico/media-centre/news-and-blogs/2022/10/immature-
    biometric-technologies
    -could-be-discriminating-against-people-says-ico-in-warning-to-
    organisations.
        \18\ Access Now, European Digital Rights (EDRi), Bits of Freedom,
    Article 19, and IT-Pol, ``Prohibit Emotion Recognition in the
    Artificial Intelligence Act,'' May 2022, https://www.access
    now.org/wp-content/uploads/2022/05/Prohibit-emotion-recognition-in-the-
    Artificial-Intelligence-Act.pdf.
        \19\ See the American Data Privacy and Protection Act, H.R. 8152,
    117th Congress, June 21, 2022, https://docs.house.gov/meetings/IF/IF00/
    20220720/115041/BILLS-1178152rh.pdf.
    ---------------------------------------------------------------------------
        (3) Business model harms. As the past decade illuminates, tech
    firms already have strong incentives for irresponsible and invasive
    data collection, fueled primarily by a business model that relies on
    personalized behavioral targeting of consumers with advertising. The AI
    boom exacerbates this, fueling a race to the bottom. In fact, a key
    feature of the current market for large-scale AI is that it is not only
    computationally, ecologically, and data intensive, it is also very,
    very expensive to develop and run these systems.\20\ These eye-watering
    costs will need a path to profit. By all accounts, though, a viable
    business model remains elusive.\21\ It is precisely in this kind of
    environment, with a few incumbent firms feeling the pressure to turn a
    profit, that predatory business models tend to emerge.
    ---------------------------------------------------------------------------
        \20\ Seth Fiegerman and Matt Day, ``Why AI Is So Expensive,''
    Bloomberg, April 30, 2024, https://www.bloomberg.com/news/articles/
    2024-04-30/why-artificial-intelligence-is-so-expensive.
        \21\ See David Cahn, ``AI's $600B Question,'' Sequoia Capital, June
    20, 2024, https://www.sequoiacap.com/article/ais-600b-question; and
    Benj Edwards, ``So Far, AI Hasn't Been Profitable for Big Tech,'' Ars
    Technica, October 10, 2023, https://arstechnica.com/information-
    technology/2023/10/so-far-ai-hasnt-been-profitable-for-big-tech.
    ---------------------------------------------------------------------------
        Finally, there is also a broader harm that cuts across the
    indiscriminate collection and retention of data at these various stages
    of the AI life cycle: the more data that is collected and stored
    indefinitely, the more we are creating ``honeypots'' or ``goldmines for
    cyber criminals''\22\ that are an attractive target for interception by
    unauthorized third parties,\23\ including malicious state and non-state
    actors. We already have examples of the real human costs of careless
    retention of data, from biometric information of Afghan citizens in
    American-managed databases that fell into the hands of the Taliban,\24\
    to the intricate web of third-party data brokers that buy and sell
    sensitive information about people that can be used to target them
    unfairly or to hinder their access to credit, housing, and
    education.\25\ Data minimization is based on the premise that
    information that's never collected in the first place cannot be
    breached; and that which is deleted after it's no longer needed is no
    longer at risk.
    ---------------------------------------------------------------------------
        \22\ Dimitri Sirota, ``The Art Of Letting Go: How Data Minimization
    Can Improve Cybersecurity And Reduce Cost,'' Forbes, March 29, 2023,
    https://www.forbes.com/sites/forbestechcouncil/2023/03/29/the-art-of-
    letting-go-how-data-minimization-can-improve-cybersecurity-and-reduce-
    cost/?sh=641958c75340.
        \23\ For examples of ``leaky'' data from Internet of things (IoT)
    devices and mobile phones, leaving personal information of users
    vulnerable to interception, see Anna Maria Mandalari et al., ``Blocking
    without Breaking: Identification and Mitigation of Non-Essential IoT
    Traffic,'' Proceedings of Privacy Enhancing Technologies Symposium
    (PETS), May 11, 2021, https://doi.org/10.48550/arXiv.2105.05162.
        \24\ Eileen Guo and Hikmat Noori, ``This Is the Real Story of the
    Afghan Biometric Databases Abandoned to the Taliban,'' MIT Technology
    Review, August 30, 2021, https://www.technolo
    gyreview.com/2021/08/30/1033941/afghanistan-biometric-databases-us-
    military-40-data-points.
        \25\ See, for example, Federal Trade Commission, ``FTC Sues Kochava
    for Selling Data That Tracks People at Reproductive Health Clinics,
    Places of Worship, and Other Sensitive Locations,'' August 29, 2022,
    https://www.ftc.gov/news-events/news/press-releases/2022/08/ftc-sues-
    kochava-selling-data-tracks-people-reproductive-health-clinics-places-
    worship-other.
    ---------------------------------------------------------------------------
        II. The turn toward large-scale AI further consolidates Big Tech's
    already staggering control over consumer data, which deepens power
    asymmetries and allows these companies to act recklessly and with
    impunity.
        Large-scale AI depends principally on data and compute resources
    (this includes both cloud computing and hardware components like chips)
    as essential inputs. Big Tech companies are already positioned at a
    considerable advantage at many points in the AI stack. Currently, the
    largest consumer technology companies such as Google, Microsoft, and
    Amazon dominate access to such compute resources (and other companies,
    as a rule, depend on them for these resources).\26\ This is closely
    related to these companies' pre-existing data advantage, which enables
    them to collect and store large amounts of good-quality data about
    billions of people via their vast market penetration.
    ---------------------------------------------------------------------------
        \26\ Jai Vipra and Sarah Myers West, ``Computational Power and
    AI,'' AI Now Institute, September 27, 2023, https://ainowinstitute.org/
    publication/policy/compute-and-ai.
    ---------------------------------------------------------------------------
        The idea that ``data is everywhere'' and therefore not a scarce
    resource is intuitively appealing but misses the point: quality data is
    scarce. Datasets with high levels of human curation and human feedback;
    niche datasets especially in high-impact sectors like finance or
    healthcare; datasets that come with assurances of accuracy, legitimacy,
    and diversity at scale are becoming a key source of competitive
    advantage for Big Tech companies, especially in the hypercompetitive
    generative AI market. This data advantage can give models developed by
    Big Tech companies an edge over those developed without the benefit of
    such data. Indeed, access to high-quality data can result in smaller
    models (those trained on less data and requiring less computational
    power for training) that perform better than larger models trained
    without such quality data. OpenAI has reportedly already used YouTube
    data to train its models, which leaves the door open for Google to use
    data not only from YouTube, but also from Gmail, Google Drive, and all
    its other properties.\27\ Similarly, Microsoft can potentially use data
    from its enterprise services, and AWS from its cloud services. Each of
    these companies has also forged partnerships and acquisitions in
    specific sectors that give them access to troves of sensitive data,
    such as in the electronic health records space.\28\
    ---------------------------------------------------------------------------
        \27\ Jon Victor, ``Why YouTube Could Give Google an Edge in AI,''
    The Information, June 14, 2023, https://www.theinformation.com/
    articles/why-youtube-could-give-google-an-edge-in-ai.
        \28\ See Karen Weise, ``Amazon to Acquire One Medical Clinics in
    Latest Push into Health Care,'' New York Times, July 21, 2022, https://
    www.nytimes.com/2022/07/21/business/amazon-one-medical-deal.html; Tina
    Reed, ``Google Cloud Announces Epic Partnership,'' Axios, November 14,
    2022, https://www.axios.com/2022/11/14/google-cloud-announces-epic-
    partnership; and Epic, ``Epic and Microsoft Bring GPT-4 to EHRs,'' May
    5, 2023, https://www.epic.com/epic/post/epic-and-microsoft-bring-gpt-4-
    to-ehrs.
    ---------------------------------------------------------------------------
        Repositories of publicly available data currently available online
    are also likely to dwindle or become less valuable soon in comparison
    to proprietary datasets held by these companies. We're already seeing a
    trend toward more restrictions on publicly available data\29\ expensive
    content deals between large AI firms and big publishers like The
    Atlantic and Axel Springer\30\ and websites like Reddit and Stack
    Overflow;\31\ and a general lack of transparency around what datasets
    are being used to train AI.\32\
    ---------------------------------------------------------------------------
        \29\ Isabelle Basquette, ``AI Startups Have Tons of Cash, but Not
    Enough Data. That's a Problem,'' Wall Street Journal, June 15, 2023,
    https://www.wsj.com/articles/ai-startups-have-tons-of-cash-but-not-
    enough-data-thats-a-problem-d69de120.
        \30\ Damon Beres, ``A Devil's Bargain with OpenAI,'' Atlantic, May
    29 2024, https://www.theatlantic.com/technology/archive/2024/05/a-
    devils-bargain-with-openai/678537.
        \31\ Associated Press, ``As AI Learns from Stack Overflow, Reddit,
    and More Platforms, Companies Are Adapting While Users Protest,'' Fast
    Company, July 3, 2024, https://www.fast
    company.com/91150665/ai-learns-stack-overflow-reddit-facebook-adapting-
    users-protest.
        \32\ See Mike Isaac, ``Reddit Wants to Get Paid for Helping to
    Teach Big A.I. Systems,'' New York Times, April 18, 2023, https://
    www.nytimes.com/2023/04/18/technology/reddit-ai-openai-google.html;
    @XDevelopers, March 29, 2023, https://x.com/XDevelopers/status/
    16412227825
    94990080; and Paresh Dave, ``Stack Overflow Will Charge AI Giants for
    Training Data,'' Wired, April 20, 2023, https://www.wired.com/story/
    stack-overflow-will-charge-ai-giants-for-training-data.
    ---------------------------------------------------------------------------
        In this environment, unlike other actors that must largely rely on
    third-party intermediaries to access data, large firms are exploiting
    the fact that they directly control the vast majority of the
    environment in which data is collected; they are able to take advantage
    of the network effects associated with the scale at which they operate
    by collecting, analyzing, and using data within platforms they wholly
    own and control.\33\ This is a product of a self-reinforcing feedback
    loop, which over time has led to these firms being so dominant and
    pervasive that it is virtually impossible not to use their systems.\34\
    ---------------------------------------------------------------------------
        \33\ Lina M. Khan, ``Sources of Tech Platform Power,'' Georgetown
    Law Technology Review 2, no.2 (2018): 325-334, https://
    georgetownlawtechreview.org/wp-content/uploads/2018/07/2.2-Khan-pp-225-
    34.pdf; Lina M. Khan, ``The Separation of Platforms and Commerce,''
    Columbia Law Review 119, no. 4 (May 2019): 973-1098, https://
    columbialawreview.org/content/the-separation-of-platforms-and-commerce.
        \34\ Kashmir Hill, ``I Tried to Live without the Tech Giants. It
    Was Impossible,'' New York Times, July 31, 2020, https://
    www.nytimes.com/2020/07/31/technology/blocking-the-tech-giants.html.
    ---------------------------------------------------------------------------
        This market reality must inform any privacy and AI-specific
    regulatory efforts. Privacy and competition law are too often siloed
    from each other,\35\ leading to interventions that could easily
    compromise the objectives of one issue over the other.\36\ And firms
    are, in turn, taking advantage of this to amass information asymmetries
    that contribute to further concentration of their power.\37\
    ---------------------------------------------------------------------------
        \35\ Udbhav Tiwari, ``Competition Should Not Be Weaponized to
    Hobble Privacy Protections on the Open Web,'' Mozilla (blog), April 12,
    2022, https://blog.mozilla.org/netpolicy/2022/04/12/competition-should-
    not-be-weaponized-to-hobble-privacy-protections-on-the-open-web.
        \36\ Maurice E. Stucke, ``The Relationship between Privacy and
    Antitrust,'' Notre Dame Law Review Reflection 97, no. 5 (2022): 400-
    417, https://ndlawreview.org/wp-content/uploads/2022/07/Stucke_97-
    Notre-Dame-L.-Rev.-Reflection-400-C.pdf.
        \37\ For example, Article 5 of the European Union's Digital Markets
    Act prohibits large ``gatekeeper'' platforms from the cross-use of
    personal data between its various service offerings, without explicit
    user consent. See European Commission, ``The Digital Markets Act:
    Ensuring Fair and Open Digital Markets,'' accessed July 9, 2024,
    https://commission.europa.eu/strategy-and-policy/priorities-2019-2024/
    europe-fit-digital-age/digital-markets-act-ensuring-fair-and-open-
    digital-markets_en.
    ---------------------------------------------------------------------------
        This concentration of power enabled by control over data isn't just
    a problem for potential competitors of Big Tech. Too much centralized
    economic power in the hands of too few harms our democracy--especially
    when these very same actors have proven themselves to be reckless and
    far from dependable custodians of this power. Amid the hype surrounding
    AI, companies are rushing to market with technologies that are far from
    ready to be broadly accessible. Google recently rolled out its AI
    Overviews feature in its search engine results; within days it was
    producing inaccurate, nonsensical, and even dangerous answers to
    people's queries.\38\ Meta's new AI agents, which the company
    integrated into millions of Instagram and Facebook accounts, have
    generated misinformation and misled people into believing they were
    interacting with real human beings.\39\ And Microsoft has been broadly
    panned for proposing a new AI-enabled feature named Recall that raises
    numerous privacy-related red flags.
    ---------------------------------------------------------------------------
        \38\ Ellie Stevens, ``The 7 Most Shocking Google AI Answers We've
    Seen So Far,'' Fast Company, May 30, 2024, https://www.fastcompany.com/
    91132974/shocking-google-ai-overview-answers.
        \39\ ``Meta's New AI Agents Confuse Facebook Users,'' Associated
    Press, April 20, 2024, https://www.voanews.com/a/meta-s-new-ai-agents-
    confuse-facebook-users-/7576420.html.
    ---------------------------------------------------------------------------
        These are the very same companies that resist regulatory guardrails
    in the name of ``innovation.'' It's time to question the premise: Is
    this scramble for reckless growth by a handful of surveillance
    monopolies really innovation? It's not surprising that these companies,
    free from regulatory constraints or competitive market pressures, are
    acting out. A data privacy mandate that embeds transparency and
    accountability around how these companies build AI, and when they
    determine they are fit for market release, isn't curbing innovation:
    it's a long overdue check on these companies.
        III. Data privacy law--in particular strong data minimization,
    impact assessments, data rights, and protections against algorithmic
    discrimination--provides a foundational toolkit for demanding
    accountability from AI companies.
        Taking stock of some of the myriad, diffused ways in which AI is
    poised to heighten threats to our individual and collective privacy,
    and worsen the concentrations of power in Big Tech, we can now ask: How
    might the AI market develop differently in the presence of a strong,
    broad-ranging Federal privacy law?
    a. Data minimization:
        Data minimization rules impose a proactive obligation on entities
    to put reasonable limits on the collection, use, and retention of
    personal data in the interest of the individual and group data holders.
    These ``data minimization'' rules, which are described in recent
    proposals such as the APRA,\40\ are a core part of global data
    protection laws. As AI Now, Accountable Tech, and EPIC emphasize in our
    ``Zero Trust AI Framework,'' data minimization rules are essential
    levers at a time when AI is tipped to further exacerbate information
    asymmetries between individuals and communities on one hand, and the
    large corporations that create and collect data about them on the
    other.\41\
    ---------------------------------------------------------------------------
        \40\ U.S. Senate Committee on Commerce, Science, and
    Transportation, ``Committee Chairs Cantwell, McMorris Rodgers Unveil
    Historic Draft Comprehensive Data Privacy Legislation,'' April 7, 2024,
    https://www.commerce.senate.gov/2024/4/committee-chairs-cantwell-
    mcmorris-rodgers-unveil-historic-draft-comprehensive-dat a-privacy-
    legislation.
        \41\ Accountable Tech, AI Now Institute, and EPIC, ``Zero Trust AI
    Governance,'' AI Now Institute, August 10, 2023, https://
    ainowinstitute.org/publication/zero-trust-ai-governance.
    ---------------------------------------------------------------------------
        Collection limitation, for example, would force firms to adhere to
    limits when it comes to data surveillance and acquisition--to build
    within the constraints of necessity and proportionality. This would
    replace reckless organizational data cultures with reflexivity that
    forces engineers to calibrate decisions about data, keeping in mind the
    privacy and security vulnerabilities these create. In response to
    Microsoft's announcement of its Recall feature that continuously
    screenshots our activities on the computer, the lawmakers and the
    public would be empowered to demand basic accountability: is such data
    surveillance, that creates a honey pot for bad actors and unauthorized
    access, at all proportionate? It is likely, in fact, that this feature
    may have never been announced in the first place had the company done
    even a rudimentary impact assessment that evaluated risks to privacy
    and security. Put simply, a strong data minimization mandate would have
    disincentivized the development of these patently unsafe features to
    begin with.
        A purpose limitation rule, on the other hand, could restrain Big
    Tech monopolies from endlessly combining data collected for distinct
    purposes in pursuit of consolidating their data advantage against
    competitors. We already have examples of these rules being applied to
    protect consumers from harm. The FTC has also penalized Amazon for
    storing children's voiceprints--highly sensitive data--and shot down
    the company's justification that it would be used to improve its Alexa
    algorithm.\42\
    ---------------------------------------------------------------------------
        \42\ Federal Trade Commission, ``U.S. v. Amazon.com (Alexa),'' FTC
    Cases and Proceedings, July 21, 2023, https://www.ftc.gov/legal-
    library/browse/cases-proceedings/192-3128-amazon
    com-alexa-us-v.
    ---------------------------------------------------------------------------
        Most crucially, data minimization rules don't hinge on user
    consent: they apply regardless, overcoming the now-well-known
    deficiencies of a privacy regime that hinges exclusively on individuals
    being able to meaningfully exercise choices online given the structural
    power asymmetries that abound between individuals and massive tech
    firms.\43\ Just this week, Open AI CEO Sam Altman, in collaboration
    with another company, announced Thrive AI Health, pitched as a ``hyper-
    personalized AI health coach.'' \44\ They set out a pervasive vision of
    data surveillance, ranging from highly intimate sleeping, eating, and
    exercise behaviors combined with medical data. The premise is that
    because individuals can ``choose'' whether to share this data, any
    privacy concerns are put to rest. This flies in the face of a decade of
    evidence that indicates we cannot rely solely on people's choices to
    protect them, when the long-term implications of unauthorized access,
    out-of-context sharing, or malicious use are difficult if not
    impossible for the average consumer to meaningfully comprehend. This is
    the outcome of a regulatory environment that has failed to place limits
    on the unrestrained collection, storage, and use of sensitive data,
    allowing companies to fall back on consent as a catchall defense.
    ---------------------------------------------------------------------------
        \43\ See Federal Trade Commission, ``Commercial Surveillance and
    Data Security Rulemaking,'' notice, August 11, 2022, https://
    www.ftc.gov/legal-library/browse/federal-register-notices/commercial-
    surveillance-data-security-rulemaking; and David Medine and Gayatri
    Murthy, ``Companies, Not People, Should Bear the Burden of Protecting
    Data,'' Brookings, December 18, 2019, https://www.brookings.edu/
    articles/companies-not-people-should-bear-the-burden-of-protecting-
    data.
        \44\ Sam Altman and Ariana Huffington, ``AI-Driven Behavior Change
    Could Transform Health Care,'' Time, July 7, 2024, https://time.com/
    6994739/ai-behavior-change-health-care.
    ---------------------------------------------------------------------------
        Beyond the general principle of data minimization, a data privacy
    law could include prohibitions on specific kinds of data use that have
    well-known harms, such as targeted advertising to children,\45\ or uses
    based on sensitive data categories,\46\ or the use of data about
    people's interior mental states in so-called ``emotion recognition''
    systems that have been repeatedly found to be based on faulty
    foundations.\47\ Perhaps, Open AI CEO Sam Altman would have likely been
    stopped in his tracks before claiming the recent multimedia chatbot
    Sora would be able to ``detect people's emotional states'' from
    people's voice recordings.
    ---------------------------------------------------------------------------
        \45\ See American Data Privacy and Protection Act, H.R. 8152, 117th
    Congress, https://docs.house.gov/meetings/IF/IF00/20220720/115041/
    BILLS-1178152rh.pdf.
        \46\ Ibid.
        \47\ Access Now, European Digital Rights (EDRi), Bits of Freedom,
    Article 19, and IT-Pol, ``Prohibit Emotion Recognition in the
    Artificial Intelligence Act.''
    ---------------------------------------------------------------------------
    b. Data rights:
        Data rights are a crucial complement to the proactive obligations
    of data minimization, as they empower individuals to ascertain the
    nature and scale of commercial surveillance, and to act on such
    information to correct, order deletion, or otherwise seek redress if
    they believe any other obligations owed to them under the legislation
    have not been fulfilled.
        Currently, the only constraint on usage of any consumer data for
    training of proprietary models comes from the terms of service of those
    products, which can be changed at will, as Google and Meta recently
    did. Notable too that while European users were alerted by Meta that it
    would use publicly available posts to train its AI, American users
    received no such notification.\48\ With a comprehensive data privacy
    law, these individuals would have, at minimum, the ability to demand
    transparency around the use of their data.
    ---------------------------------------------------------------------------
        \48\ Eli Tan, ``When the Terms of Service Change to Make Way for
    A.I. Training.''
    ---------------------------------------------------------------------------
        Under the latest text of APRA, consumers would also have a broad
    right to opt out of algorithmic decision-making that comprises
    ``consequential decisions''--defined as decisions, including ads, that
    may impact an individual's equal access to housing, employment,
    healthcare, and so on.\49\ Such algorithmic decision-making is
    ubiquitous today, with limited oversight. As just one example, an IBM
    survey in 2023 showed that out of 8,500 participants in the survey, 42
    percent were already using AI screening to filter out candidates, and
    another 40 percent were in the process of integrating with such
    technology.\50\ These screening softwares have been known to filter out
    qualified candidates based on their age, gender, or even hobbies, with
    marginalized candidates bearing the brunt of maximum harm.\51\ A right
    to opt out of consequential decisions would allow these candidates a
    fairer review of their applications and would force companies to put
    better pipelines to employment in place that do not exacerbate
    entrenched inequalities in the workplace.
    ---------------------------------------------------------------------------
        \49\ U.S. Senate Committee on Commerce, Science, and
    Transportation, ``Committee Chairs Cantwell, McMorris Rodgers Unveil
    Historic Draft Comprehensive Data Privacy Legislation,'' April 7, 2024,
    https://www.commerce.senate.gov/2024/4/committee-chairs-cantwell-
    mcmorris-rodgers-unveil-historic-draft-comprehensive-dat a-privacy-
    legislation.
        \50\ ``Data Suggests Growth in Enterprise Adoption of AI is Due to
    `Widespread Deployment by Early Adopters, But Barriers Keep 40 percent
    in the Exploration and Experimentation Phase,' '' IBM Newsroom, January
    10, 2024, https://newsroom.ibm.com/2024-01-10-Data-Suggests-Growth-in-
    Enterprise-Adoption-of-AI-is-Due-to-Widespread-Deployment-by-Early-
    Adopters.
        \51\ Aaron Rieke and Miranda Bogen, ``Help Wanted: An Examination
    of Hiring Algorithms, Equity, and Bias,'' Upturn, December 10, 2018,
    https://www.upturn.org/work/help-wanted.
    ---------------------------------------------------------------------------
    c. Impact Assessments:
        A data privacy law should also include a mandate for impact
    assessments or audits of AI systems in order to proactively identify
    and mitigate harms, including relating to discrimination, privacy, and
    security. These evaluations go beyond conventional privacy impact
    assessments that assess systems against relatively narrow privacy and
    security criteria, in favor of a more expansive stocktaking that
    requires companies to evaluate whether particular groups will be harmed
    as a result of the design or use of the AI system. Researchers like Dr.
    Alex Hanna and Dr. Mehtab Khan, for example, have put forward a
    multilayered framework to scrutinize the multiple complex layers of
    large-scale AI models.\52\
    ---------------------------------------------------------------------------
        \52\ Mehtab Khan and Alex Hanna, ``The Subjects and Stages of AI
    Dataset Development: A Framework for Dataset Accountability,'' Ohio
    State Technology Law Journal, September 13, 2022, http://dx.doi.org/
    10.2139/ssrn.4217148.
    ---------------------------------------------------------------------------
        One could imagine that some of the most concerning recent AI
    features and products, from Microsoft's Recall to Google AI Overviews,
    would perhaps never have been announced or brought to market had firms
    been required to comprehensively evaluate the privacy and security
    implications of their systems before release.
        A note of caution on impact assessments: while such evaluations are
    positive in theory, these obligations must be drafted to ensure
    meaningful accountability. There is a significant risk that any audit
    or evaluation standard can devolve into a superficial checkbox
    exercise,\53\ more useful in offloading liability than in protecting
    the public. With that in mind, we recommend the following:
    ---------------------------------------------------------------------------
        \53\ See Amba Kak and Sarah Myers West, Algorithmic Accountability:
    Moving Beyond Audits, AI Now Institute, April 11, 2023, https://
    ainowinstitute.org/publication/algorithmic-accountability; and Sasha
    Costanza-Chock, Inioluwa Deborah Raji, and Joy Buolamwini, ``Who Audits
    the Auditors? Recommendations from a Field Scan of the Algorithmic
    Auditing Ecosystem,'' FAccT '22: Proceedings of the 2022 ACM Conference
    on Fairness, Accountability, and Transparency, June 2022, https://
    doi.org/10.1145/3531146.3533213. Professor Woody Hartzog refers to
    audits and similar procedural interventions as ``privacy half
    measures'' that are necessary but wholly insufficient in protecting
    users; see Hearing On ``Oversight Of A.I.: Legislating On Artificial
    Intelligence'' Before the Subcommittee On Privacy, Technology, And The
    Law, U.S. Senate Committee On The Judiciary, September 12, 2023
    (Statement of Woodrow Hartzog), https://techpolicy.press/wp-content/
    uploads/2023/09/2023-09-12_pm_-_testimony_-_hartzog.pdf.

       Meaningful assessments that mandate evaluation should happen
            before products are made available for use in the public
            domain, and should be subject to evaluation on an ongoing basis
            while in operation. It is essential that the criteria for such
            evaluations not be limited to narrow technical parameters or be
            tested only under so-called ``laboratory-like conditions.''
            \54\
    ---------------------------------------------------------------------------
        \54\ See Ben Green and Lily Hu, ``The Myth in the Methodology:
    Towards a Recontextualization of Fairness in Machine Learning,'' 35th
    International Conference on Machine Learning, 2018, https://
    econcs.seas.harvard.edu/files/econcs/files/green_icml18.pdf; Shira
    Mitchell, Eric Potash, Solon Barocas, Alexander D'Amour, and Kristian
    Lum, ``Algorithmic Fairness: Choices, Assumptions, and Definitions,''
    Annual Review of Statistics and Its Application 8 (2021): 141-163,
    https://doi.org/10.1146/annurev-statistics-042720-125902; and Rodrigo
    Ochigame, ``The Long History of Algorithmic Fairness,'' Phenomenal
    World, January 30, 2020, https://www.pheno
    menalworld.org/analysis/long-history-algorithmic-fairness.

       Evaluations must be conducted by independent, disinterested,
            and adequately resourced and protected third parties such as
            researchers, civil society, or the appropriate Federal
            agencies, by charging that such evaluations are subject to both
    ---------------------------------------------------------------------------
            regulatory and public scrutiny.

       There must be real consequences for a failure to mitigate or
            prevent harms that are identified. This includes strict
            penalties but also, crucially, abandoning systems that are
            designed in ways that make such harms inevitable.
    d. Prohibition against discrimination:
        A range of privacy proposals, both in the United States and
    globally, include protections against using personal data in AI in ways
    that discriminate.\55\ It is now well documented that AI systems are
    routinely, and often structurally, biased in ways that entrench and
    embed historical inequities\56\ in sensitive social domains like
    healthcare,\57\ hiring,\58\ education,\59\ housing,\60\ and criminal
    justice.\61\ This should not come as a surprise given that these
    systems necessarily draw their map of ``the world'' from data that
    reflects discriminatory histories and sentiments. As recently
    highlighted in the fact sheet accompanying the Biden administration's
    Blueprint for an AI Bill of Rights, several Federal agencies are
    already applying existing laws and mechanisms to address algorithmic
    discrimination in housing, employment, and other realms.\62\ A civil
    rights provision in a Federal privacy law would provide an overarching
    means of redress against AI systems that perpetuate discrimination.
    ---------------------------------------------------------------------------
        \55\ U.S. Senate Committee on Commerce, Science, and
    Transportation, ``Committee Chairs Cantwell, McMorris Rodgers Unveil
    Historic Draft Comprehensive Data Privacy Legislation.''
        \56\ See Federal Trade Commission, ``Joint Statement on Enforcement
    Efforts Against Discrimination and Bias in Automated Systems,'' public
    statement, April 25, 2023, https://www.ftc.gov/legal-library/browse/
    cases-proceedings/public-statements/joint-statement-enforcement-
    efforts-a gainst-discrimination-bias-automated-systems; White House,
    ``Blueprint for an AI Bill of Rights,'' August 2022, https://
    www.whitehouse.gov/ostp/ai-bill-of-rights; Samir Jain, ``CDT and
    Coalition Urge White House to Ensure Forthcoming AI Executive Order
    Advances Civil Rights & Civil Liberties,'' Center for Democracy &
    Technology, September 5, 2023, https://cdt.org/insights/cdt-and-
    coalition-urge-white-house-to-ensure-forthcoming-ai-executive-order-
    advances-civil-rights-civil-liberties.
        \57\ Ziad Obermeyer et al., ``Dissecting Racial Bias in an
    Algorithm Used to Manage the Health of Populations,'' Science 366
    (October 25, 2019): 447-453, https://www.science.org/doi/10.1126/
    science.aax2342.
        \58\ See U.S. Equal Employment Opportunity Commission, ``Artificial
    Intelligence and Algorithmic Fairness Initiative,'' January 23, 2023,
    https://www.eeoc.gov/ai; Pauline T. Kim, ``Data-Driven Discrimination
    at Work,'' 58 William & Mary Law Review 857, February 1, 2017, https://
    scholarship.law.wm.edu/wmlr/vol58/iss3/4; Ifeoma Ajunwa, ``Algorithms
    at Work: Productivity Monitoring Applications and Wearable Technology
    as the New Data-Centric Research Agenda for Employment and Labor Law,''
    63 St. Louis University Law Journal 21 (2019), September 10, 2018,
    https://ssrn.com/abstract=3247286; and Aaron Rieke and Miranda Bogen,
    ``Help Wanted.''
        \59\ See Kristin Woelfel, Elizabeth Laird, and Maddy Dwyer,
    ``Letter to ED and the White House from Tech Policy, Civil Rights, and
    Civil Liberties Advocates Calling for Civil Rights Guidance and
    Enforcement Regarding EdTech and AI,'' Center for Democracy &
    Technology, September 20, 2023, https://cdt.org/insights/letter-to-ed-
    and-the-white-house-from-tech-policy-civil-rights-and-civil-liberties-
    advocates-calling-for-civil-rights-guidance-and-enforcement-regarding-
    edtech-and-ai; and Andre M. Perry and Nicol Turner Lee, ``AI Is Coming
    to Schools, and If We're Not careful, So Will Its Biases,'' Brookings,
    September 26, 2019, https://www.brookings.edu/articles/ai-is-coming-to-
    schools-and-if-were-not-careful-so-will-its-biases.
        \60\ See U.S. Justice Department, ``Justice Department Secures
    Groundbreaking Settlement Agreement with Meta Platforms, Formerly Known
    as Facebook, to Resolve Allegations of Discriminatory Advertising,''
    press release, June 21, 2022, https://www.justice.gov/opa/pr/justice-
    department-secures-groundbreaking-settlement-agreement-meta-platforms-
    formerly-known; Lauren Kirchner and Matthew Goldstein, ``Access Denied:
    Faulty Automated Background Checks Freeze Out Renters,'' The Markup,
    May 28, 2020, https://themarkup.org/locked-out/2020/05/28/access-
    denied-faulty-automated-background-checks-freeze-out-renter; Ridhi
    Shetty, ``CDT Comments to Federal Agencies Highlight Risks of Data Used
    in Tenant Screening,'' Center for Democracy & Technology, June 2, 2023,
    https://cdt.org/insights/cdt-comments-to-federal-agencies-highlight-
    risks-of-data-used-in-tenant-screening.
        \61\ Julia Angwin, Jeff Larson, Surya Mattu and Lauren Kirchner,
    ``Machine Bias,'' ProPublica, May 23, 2016, https://www.propublica.org/
    article/machine-bias-risk-assessments-in-criminal-sentencing.
        \62\ White House, ``Blueprint for an AI Bill of Rights.''
    ---------------------------------------------------------------------------
        To conclude, the key lesson of the past decade has been
    understanding that control over data is about power asymmetries, and
    since companies derive clear commercial benefit from widening this
    asymmetry, regulation is essential to protect the public from harm.
    Passing strong Federal privacy legislation is a critical and overdue
    step in that direction.\63\ And while it is true that the United States
    is already behind in terms of enacting a comprehensive data privacy
    law, in those countries that have these legislative mandates in place,
    there have been major gaps and ambiguities in implementation. An
    opportunity exists, therefore, to enact and creatively apply
    foundational privacy principles to the emergent landscape of AI
    systems, setting the gold standard of enforcement for the rest of the
    world.
    ---------------------------------------------------------------------------
        \63\ Accountable Tech, AI Now Institute, and EPIC, ``Zero Trust AI
    Governance.''

        The Chair. Thank you, Ms. Kak. Thank you for that
    testimony.
        And I--the notion that--in your testimony you talked about
    how just the sound of our voice could be used to project
    different detections and different outcomes of what people are
    doing is just very disturbing. Very disturbing.
        So, Mr. Tiwari. Welcome.

                 STATEMENT OF UDBHAV TIWARI, DIRECTOR,
                     GLOBAL PRODUCT POLICY, MOZILLA

        Mr. Tiwari. Chair Cantwell, Ranking Member Cruz, and
    esteemed members of the Committee, thank you for the
    opportunity to testify on the critical issue of protecting
    Americans' privacy in the age of artificial intelligence.
        My name is Udbhav Tiwari and I am the director of global
    product policy at Mozilla. Today I will discuss the urgent need
    for comprehensive privacy legislation, the importance of data
    minimization, and the role of privacy-enhancing technologies in
    fostering responsible AI development.
        At Mozilla we approach tech policy issues through a unique
    vantage point as a nonprofit foundation, an open source
    community, and a tech company. We build the open source Firefox
    web browser Mozilla VPN and products like Solo, an AI-powered
    website builder for micro SMBs and solopreneurs.
        These products are used by hundreds of millions of people
    around the world and Mozilla's mission is to ensure that the
    Internet is a global public resource open and accessible to
    all.
        We believe that comprehensive privacy legislation is
    foundational to any AI framework and that maintaining U.S.
    leadership in AI requires America to lead on privacy and user
    rights.
        Privacy is a critical component of AI policy, not just
    because AI has the potential to accelerate privacy-related
    harms but because AI systems thrive on data and the drive to
    develop advanced AI models has also intensified the demand for
    vast amounts of personal information.
        This data collection, often done without the adequate
    consent or via deceptive choices, poses significant risks to
    individual privacy and security.
        By championing policies that promote innovation, create
    clear rules of the road for companies, and protect fundamental
    user rights, we can create both a competitive and a level
    playing field for the American AI industry and prepare domestic
    champions for global leadership.
        At the core of these principles and policies should be data
    minimization. Data minimization is a crucial principle that
    ensures only the necessary data is collected and used for
    specific purposes. In the context of AI data minimization can
    be achieved through several strategies including informed
    consent and ensuring there is privacy by design.
        While legislation is essential, technical advances must
    work hand in hand with legislative solutions to create a new
    safe and private future. In the context of what the ecosystem
    currently needs we need a significant investment in privacy-
    enhancing technologies to develop AI systems that respect and
    protect individual privacy.
        Openness and open source are also essential ingredients for
    improving verifiable and meaningful privacy in AI technologies.
    Open approaches play a vital role in promoting innovation and
    preventing the concentration of power in the hands of few
    companies that my fellow panelists have spoken about.
        They enable the economic benefit of AI to be more widely
    shared amongst businesses of different sizes and capabilities.
    This leads to increased investment and job creation.
        We also have clear evidence from open source development
    practices that openness allows a diverse input and
    collaboration, fostering the development of privacy preserving
    techniques that can benefit everyone rather than relying on
    security through obscurity.
        Turning to the potential risks of AI amplifying privacy
    violations, we know that online manipulation, targeted scams,
    and online surveillance are not new risks in our digital lives.
        However, AI technologies can supercharge such harms,
    creating risks like profiling and manipulation, bias and
    discriminations, and deep fakes and identity misuse.
        To mitigate these risks we need comprehensive Federal
    privacy legislation. This should be accompanied by strong
    regulatory oversight and continued investments in privacy-
    enhancing technologies.
        We must also ensure that AI systems are transparent and
    accountable with mechanisms in place to address privacy
    violations and provide recourse for affected individuals
    underpinned by disclosure and accountability.
        When it comes to the AI's potential to impact civil
    liberties the risk cannot be understated. The same technologies
    that drive innovation can also be used to infringe upon
    fundamental rights and be used by big tech companies to trample
    on individual privacy.
        It is therefore imperative that AI development and
    deployment are guided by principles that protect civil
    liberties. This includes safeguarding freedom of expression,
    preventing unlawful surveillance, and ensuring that AI systems
    do not perpetuate discrimination or bias.
        In conclusion, protecting Americans' privacy in the age of
    AI is a critical challenge that requires comprehensive
    legislation. As we navigate the complexities of AI and privacy
    it is crucial to strike a balance between innovation and
    protection.
        Thank you for the opportunity to testify today. I look
    forward to your questions and to working with you to protect
    Americans' privacy in the AI era.
        [The prepared statement of Mr. Tiwari follows:]

     Prepared Statement of Udbhav Tiwari, Director, Global Product Policy,
                                    Mozilla
        Chair Cantwell, Ranking Member Cruz, and esteemed members of the
    Committee,

        Thank you for the opportunity to testify on the critical issue of
    protecting Americans' privacy in the age of artificial intelligence
    (AI). My name is Udbhav Tiwari, and I am the Director of Global Product
    Policy at Mozilla. Today, I will discuss the urgent need for
    comprehensive privacy legislation, the importance of data minimization,
    and the role of privacy-enhancing technologies in fostering responsible
    AI development. America can continue to be a leader in AI by putting in
    place clear rules of the road on privacy that will help to spur
    beneficial competition and create a level playing field for players of
    all sizes instead of a race to the bottom.
    About Mozilla
        At Mozilla, we approach tech policy issues from a unique vantage
    point as a non-profit foundation, open-source community, and a tech
    company. We build the open-source Firefox web browser, Mozilla VPN, and
    products like Solo, an AI-powered website builder for micro-SMBs and
    solopreneurs. These products are used by hundreds of millions of
    individuals around the world. Mozilla's mission is to ensure the
    Internet is a global public resource, open and accessible to all. To
    fulfill this mission, we are constantly investing in the security of
    our products, the privacy of our users and in advancing the movement to
    build a healthier internet. This includes supporting research in areas
    like competition and the impact of harmful social media recommendations
    that have helped to inform policymakers around the world. Mozilla has
    influenced major companies to adopt better privacy practices and
    empowered people directly with tools to better understand and protect
    their data online. For Mozilla, individuals' security and privacy on
    the Internet are fundamental rights and must not be treated as
    optional.
        With this goal, for the past five years Mozilla has been committed
    to advancing trustworthy AI. Mozilla recently published a paper,
    Accelerating Progress Toward Trustworthy AI, that outlines how Mozilla
    and our allies are advancing openness, competition, and accountability
    in AI. Mozilla is putting its resources behind these priorities as
    well: The Mozilla Foundation, which is the full owner of the Mozilla
    Corporation, has been dedicating 100 percent of its $30M a year budget
    to philanthropic activities, advocacy, and programmatic work on this
    topic. Mozilla is also investing another $30M in research and
    development on trustworthy AI, $35M in responsible tech startups--
    including startups with a focus on trustworthy AI--through Mozilla
    Ventures, and building the data infrastructure needed to create AI that
    works in multiple languages via Common Voice. We're seeking to help
    ensure that every person and every community can safely build, use, and
    assess AI.
    The Imperative of Comprehensive Privacy Legislation
        At Mozilla, we believe that comprehensive privacy legislation is
    foundational to any sound AI framework. Without such legislation, we
    risk a ``race to the bottom'' where companies compete by exploiting
    personal data rather than safeguarding it. Maintaining U.S. leadership
    in AI requires America to lead on privacy and user rights.
        Privacy is a critical component of AI policy, not just because AI
    has the potential to accelerate privacy related harms, but because at
    its heart, AI is made possible by the utilization of tremendous amounts
    of data. How that data is collected today by AI companies varies, but
    there is little question that AI, especially generative AI, has created
    a dynamic that pushes companies to collect as much data as possible,
    creating a race to accumulate vast swaths of data.
        In this context, any additional data a company can collect,
    including proprietary and personal data, could become a key competitive
    advantage as companies try to create ``data moats,'' to ward off would-
    be competitors. This means that without regulation, business incentives
    will drive companies to collect more and more data however they can.
    For example, big tech companies could collect even more user data to
    train AI models, while data brokers would be incentivized to scoop up
    additional data to sell to third parties with AI ambitions, as the
    value of data increases.
        AI systems thrive on data, and the drive to develop advanced AI
    models has intensified the demand for vast amounts of personal
    information. This data collection, often done without adequate consent
    or deceptive choices, poses significant risks to individual privacy and
    security.
        By championing policies that promote innovation, create clear rules
    of the road for companies, and protect fundamental user rights, we can
    create both a competitive and level playing field for the American AI
    industry and prepare domestic champions for global leadership. At the
    core of these policies should be data minimization.
    Data Minimization: A Cornerstone of Privacy and AI Policy
        Data minimization is a crucial principle that ensures only the
    necessary data is collected and used for specific purposes--minimizing
    risk for business and enhancing consumer trust. This approach reduces
    the risk of privacy breaches, limits the potential misuse of personal
    information, and mitigates the burden of consumers having to defend
    their own privacy. In the context of AI, data minimization can be
    achieved through several strategies including:

      1.  Informed Consent: Individuals must be meaningfully informed about
            how their data will be used and be asked explicit consent when
            their personal data is used to train AI models. This
            transparency builds trust and empowers users to make informed
            decisions about their personal information while providing
            clarity to businesses. Mozilla has campaigned on behalf of
            consumers to push the industry to do better when it comes to
            transparency.
      2.  Privacy by Design: Integrating privacy considerations into the
            design and development of AI systems ensures that privacy is
            not an afterthought but a fundamental component. This holistic
            approach of privacy by design--encompassing the entire AI
            lifecycle, from data collection to deployment--is a core
            element of Mozilla's recent ``Privacy for All'' campaign.
    Investment in Privacy-Enhancing Technologies
        While legislation is essential, technical advances must work hand-
    in-hand with them to create a more safe and private future. The
    ecosystem needs significant investment in privacy-enhancing
    technologies (PETs) to develop AI systems that respect and protect
    individual privacy. PETs enable innovative solutions that reduce risk
    in the AI lifecycle while ensuring privacy is maintained without
    stifling technological progress. For example, PETs can enable training
    and processing on-device or obfuscate machine learning outputs via
    differential privacy to mitigate the risks of re-identification.
        At Mozilla, we are committed to advancing privacy-preserving
    approaches in both the browser and for AI in general. We are focused on
    developing tools that prioritize user privacy by running on user
    devices (via projects such as Llamafile) and by developing technologies
    such as Interoperable Private Attribution (IPA) that minimize the need
    for data collection via browsers by leveraging multi-party computation.
    Local or on-device processing enables AI models to run on local devices
    rather than centralized cloud servers, significantly reducing the
    amount of data transmitted and stored by service providers. Mozilla's
    AI-enabled translation feature in Firefox, for example, performs
    translations locally using machine learning--ensuring that user data
    (such as page URLs and content) is not sent to either Mozilla or third-
    party servers who can then use data collected for their own purposes.
    The Role of Openness in Privacy-Preserving AI
        Openness is an essential ingredient for improving verifiable and
    meaningful privacy in AI technologies. While there are different
    degrees in the spectrum of openness, making AI components and systems
    more openly available to the wider community, improves transparency.
    Transparency, in turn, enables scrutiny--without which we have little
    ability to govern and hold the current large models to any privacy
    standards we might expect of other online businesses.
        Without knowing what data is being collected and how it is being
    leveraged by AI systems, we will have little ability to govern the
    technology effectively in the interests of consumers. Without
    incentives that encourage openness across the AI stack, a handful of
    dominant companies will win the race to the bottom, while citizens lose
    any effective means of control over their privacy.
        Open approaches also play a vital role in promoting innovation,
    preventing the concentration of power in the hands of a few companies.
    They enable the economic benefit of AI to be more widely shared,
    amongst businesses of different sizes and capabilities--leading to
    increased investment and job creation. We also have clear evidence from
    open source development practices, that openness allows for diverse
    input and collaboration, fostering the development of privacy-
    preserving techniques that can benefit everyone rather than relying on
    security through obscurity.
        Mozilla's commitment to openness is exemplified by our efforts to
    enable AI models to run on private devices with private data, via
    projects such as Llamafile. This reduces the need for data to be sent
    to centralized servers, mitigating privacy risks and promoting user
    control.
    AI Can Amplify Privacy Violations
        Online manipulation, targeted scams, and online surveillance are
    not new risks in our digital lives. Bad actors often seize on any
    opportunity they can, whether through spam e-mails or sophisticated
    deep fakes, to harm the average American. However, AI technologies can
    supercharge such harms by enabling personalization and scale with much
    lower barriers to access such capabilities than previously possible. AI
    technologies introduce unique privacy challenges that must be addressed
    proactively, where some of the most pressing concerns include:

       Profiling and Manipulation: AI can infer sensitive
            attributes about individuals, leading to potential privacy
            violations if used for targeted content or discrimination. This
            is especially true for advertising, a field where AI and
            machine learning have already been leveraged for years to
            predict the wants and desires of unsuspecting consumers. In
            addition, the growth of generative AI has led to advertisers
            creating highly customized campaigns, from text to images to
            videos, raising the likelihood of hyper-targeted manipulation
            at low costs.

       Consent and Data Rights: Using personal data to train AI
            models raises questions about consent, especially when
            individuals are unaware their data is being used for training.
            In the case of sensitive categories, such as kids or health
            data, existing protections need to be updated for newer use
            cases that AI enables. These updates can include stricter
            anonymization standards, inclusion of data inferred by AI
            systems (such as behavioral profiles and predictive analytics),
            and improved disclosure requirements for when such sensitive
            data is leveraged for training AI models.

       Bias and Discrimination: AI systems trained on biased data
            can perpetuate and amplify these biases, resulting in
            discriminatory outcomes. We've already seen prominent companies
            be taken to court for such practices by the U.S. government and
            AI will only create more avenues for such algorithmic
            discrimination.

       Data Exploitation: Generative AI systems trained on vast
            datasets may inadvertently reveal private information, posing
            risks to the privacy and security of the average American or
            for businesses whose employees use such systems. For example--
            employees using a cloud-based generative AI platform to create
            meeting notes by feeding in potentially sensitive or
            confidential information could inadvertently lead to that
            platform leaking that data to other parties in the future,
            something we've already seen occur in the recent past.

       Deepfakes and Identity Misuse: AI-generated content can
            convincingly depict individuals doing things they never did,
            threatening privacy and reputation.

        To mitigate these risks, we need comprehensive Federal privacy
    legislation, strong regulatory oversight, and continued investment in
    PETs. We must also ensure that AI systems are transparent and
    accountable, with mechanisms in place to address privacy violations and
    provide recourse for affected individuals, underpinned by disclosure.
    Protecting Civil Liberties in the Age of AI
        AI's potential to impact civil liberties cannot be understated. The
    same technologies that drive innovation can also be used to infringe
    upon fundamental rights and used by big tech companies to trample
    individuals' privacy. Therefore, it is imperative that AI development
    and deployment are guided by principles that protect civil liberties.
    This includes safeguarding freedom of expression, preventing unlawful
    surveillance, and ensuring that AI systems do not perpetuate
    discrimination or bias.
        At Mozilla, we have long championed the protection of civil
    liberties. We believe that privacy is not just a feature but a
    fundamental right that must be upheld in all technological
    advancements. Our commitment to privacy preserving data practices and
    AI reflects our dedication to protecting users' civil liberties in the
    digital age.
    The Path Forward
        As we navigate the complexities of AI and privacy, it is crucial to
    strike a balance between innovation and protection. Regulation must be
    designed to address the root causes of AI-enabled societal harms
    without entrenching the position of a few dominant players. We should
    avoid restrictive licensing regimes that could stifle competition and
    innovation, particularly for small and medium-sized enterprises (SMEs)
    and open-source developers.
        Instead, we need a regulatory framework that promotes responsible
    AI development and deployment, safeguards individual privacy, and
    fosters a diverse and competitive AI ecosystem. This includes urgently
    creating clear rules and enforcement mechanisms to stop bad actors from
    exploiting the innate privacy rights of Americans' online. America can
    continue to be a leader in AI by putting in place clear rules of the
    road on privacy and data protection that will help to spur beneficial
    competition and create a level playing field for players of all sizes
    instead of a race to the bottom. The improved consumer trust engendered
    by better privacy practices leads to increased brand loyalty, enhancing
    the competitive edge that American technology companies currently enjoy
    globally. Robust privacy practices also attract international
    partnerships and investments, positioning businesses to compete more
    effectively in the global marketplace--where privacy is increasingly a
    competitive differentiator.
    Conclusion
        In conclusion, protecting Americans' privacy in the age of AI is a
    critical challenge that requires comprehensive legislation, policies
    that support openness, investment in privacy-enhancing technologies,
    and a commitment to data minimization. At Mozilla, we are dedicated to
    advancing privacy-preserving AI and advocating for policies that
    promote innovation while safeguarding individual rights.
        We urge Congress to pass binding Federal privacy legislation and
    enforce strong privacy regulations. By doing so, we can ensure that AI
    development proceeds in a manner that respects privacy, promotes trust,
    and benefits all Americans.
        Thank you for the opportunity to testify today. I look forward to
    your questions and to working with you to protect Americans' privacy in
    the AI era.

        The Chair. Thank you, Mr. Tiwari. I very much appreciate
    you being here.
        And, Mr. Reed, thank you so much for being here. I am not
    trying to ask you a question in advance but I am pretty sure
    you have thousands of members of your organization, and I am
    pretty sure you have quite a few in the Pacific Northwest.
        So thank you for being here.

                 STATEMENT OF MORGAN REED, PRESIDENT,
                       ACT | THE APP ASSOCIATION

        Mr. Reed. We do, many in the great state of Washington.
        Chair Cantwell, Ranking Member Cruz, and members of the
    Committee, my name is Morgan Reed, President of ACT | The App
    Association, a trade association representing small and medium
    sized app developers and connected device manufacturers.
        Thank you for the opportunity to testify today on two
    significant and linked subjects, privacy and AI.
        Let me say very clearly that the U.S. absolutely needs a
    Federal privacy law. For years we have supported the creation
    of a balanced, bipartisan framework that gives consumers
    certain protections and businesses clear rules of the road.
        Instead, what we have now is a global array of mismatched
    and occasionally conflicting laws including here in the U.S.
    with either 19 or 20, depending on who you ask, state-level
    comprehensive privacy laws and more coming every year.
        To prevent this morass of confusion and paperwork,
    preemption must also be strong and without vague exceptions and
    it must include small businesses so that customers can trust
    the data is being protected when they do business with a
    company of any size.
        Unfortunately, the American Privacy Rights Act, or APRA,
    falls short of both of these objectives. Carving out small
    businesses from the definition of covered entities as APRA does
    is a nonstarter because it would deny us the benefits of the
    bill's preemption provisions.
        Instead, small business would be required to comply
    separately with 19 state laws and, more importantly, the not
    yet passed laws in 31 states, exposing us to costly state-by-
    state compliance and unreasonably high litigation costs.
        And it is not just small tech impacted by this. In today's
    economy everyone uses customers' data, even bricks and mortar.
    A local bike shop in Nevada likely has customers coming from
    Utah, Arizona, California, Colorado, and Idaho. An alert
    reminding these customers about a tire sale with free shipping
    or that it is time to get their bike in for a tune up requires
    at least a passing familiarity with each state's small business
    carve out.
        In the ultimate irony, APRA may even incent small
    businesses to sell customers' data in order to gain the benefit
    of preemption. Congress must instead move forward with a
    framework that incorporates small businesses and creates a path
    to compliance for them.
        And this acceptance of small businesses' role in the tech
    ecosystem becomes even more pronounced when we turn to AI.
    Mainstream media is all abuzz about big companies like Amazon,
    Microsoft, Google, and Apple moving into AI but the reality is
    small business has been the faster adopter.
        More than 90 percent of my members use generative AI tools
    today with an average 80 percent increase in productivity and
    our members who develop those AI solutions are more nimble than
    larger rivals.
        We are developing, deploying, and adapting AI tools in
    weeks rather than months. Their experiences should play a major
    role in informing policymakers on how any new laws should apply
    to AI's development and use.
        Here are two examples of how our members are using AI in
    innovative ways. First up is our Iowa-based SwineTech. It is
    reshaping the management of hog farms.
        Farmers use their product PigFlow--that is really the
    name--to manage the entire process of using sensors to track
    the health of a thousand piglets and then market analytics and
    public information to build AI-powered holistic solutions.
        But, as Paul Simon said, ``big and fat, a pig's supposed to
    look like that.'' For our member MetricMate in Atlanta the goal
    is the opposite. This Atlanta-based startup uses a combination
    of off-the-shelf and custom fitness trackers to help
    individuals and physical therapists to track and refine fitness
    goals.
        AI helped MetricMate respond to the progress being made
    over time and instantaneously while their patented tap sensor
    gives the user the ability to track their workouts and
    seamlessly transmit data to the MetricMate app.
        These examples show how AI is useful for innovative yet
    normal activities. So rather than limit AI as a whole,
    policymakers must target regulation to situations where a
    substantial risk of concrete harm exists.
        The risk of AI use on a pig farm should not be treated the
    same as risks in sectors like health care or wellness, and yet
    both of them might be covered by future laws.
        Finally, I want to stress the importance of standards to
    the future of AI. Standards are a valuable way for new
    innovators to make interoperable products that compete with the
    biggest market players.
        As the Committee considers bills on the subject we need
    NIST to remain a supporter rather than an arbiter of voluntary
    industry-led standards. The committee should also be aware of
    the threat to small business through standard essential patent
    abuse.
        With non-U.S.-based companies holding the crown for the
    most U.S. patents every year, Federal policy must combat abuse
    of patent licensing in standards by ensuring that licenses are
    available to any willing licensee including small businesses on
    fair, reasonable, and nondiscriminatory terms.
        If we are not capable of doing that the next generation of
    AI standards will not be owned or run through U.S. companies
    but through those outside of the U.S. with different
    perspectives on human rights and our expectations.
        So thank you for your time, and I look forward to answering
    your questions.
        [The prepared statement of Mr. Reed follows:]

    Prepared Statement of Morgan Reed, President, ACT | The App Association
    I. Introduction
        Small app companies and connected device makers use, create, or
    adapt artificial intelligence (AI) tools every day. Their activities
    involve a variety of different kinds of AI, from machine learning and
    deep learning to generative AI tools such as foundation models (FMs),
    large, medium, and small language models (LMs), and image generators.
    In creating, tuning, and leveraging these tools, ACT | The App
    Association's (the App Association's) members are keenly aware of the
    risks these tools may present, including how their processing
    activities meet customers', clients', and users' privacy and security
    expectations. In turn, innovators in the app economy must be able to
    rely on business partners, distributors, and online marketplaces that
    enable the best privacy and security protections.
        AI refers not to a single program or use case, but to a broad
    category of general-purpose technologies that companies and consumers
    are applying to new challenges every day. Because of this, policymakers
    must narrowly target government intervention to situations where a
    substantial risk of concrete harm exists, tailored to the harms at
    issue. Just as Congress declined to create a specific Federal agency to
    regulate combustion engines in all their manifestations, it should also
    focus its AI efforts on how the technology is used and provide the
    flexibility to scale measures taken to address the risks presented by
    different scenarios. For example, the risks presented by AI used to
    improve the quality of pizza are fundamentally of a different nature
    than those presented by clinical decision support tools in healthcare.
    Consistent with our policy principles on AI (see Appendix),\1\ Congress
    must first look to existing statutes, regulations, and best practices
    and understand how they apply to emerging technologies before leaping
    forward with legislation. We agree with several Federal agencies that
    recently affirmed that their jurisdictional boundaries do not end where
    the use of AI begins. There is no ``AI shaped hole'' in the Federal
    Trade Commission (FTC) Act; unfair or deceptive acts or practices
    taking place with the help of AI or regarding AI services are just as
    prohibited as their unassisted analogues. Overall, the risks AI poses
    are not fundamentally new and overbroad intervention to control the
    development of the technology is unlikely to produce the results
    policymakers seek.
    ---------------------------------------------------------------------------
        \1\ ACT | THe App Association, Policy Recommendations For AI,
    available at https://actonline.org/wp-content/uploads/Policy-
    Recommendations-for-AI.pdf.
    ---------------------------------------------------------------------------
        The scope of this hearing is appropriate, as the development and
    use of AI adds to the urgency for Congress to legislate on privacy in
    particular, while some other areas are not as ripe for substantive
    intervention. Our testimony makes three overarching points:

      1.  The United States needs a Federal privacy law. On the one hand,
            the United States is several steps ahead of other countries in
            the development of AI sectors, in part because it has thus far
            declined to overregulate privacy. On the other hand, our
            innovation economy could fare even better on the global stage
            if Congress were to enact a strong, preemptive Federal privacy
            framework that bolsters trust in cutting-edge AI tools while
            curbing mismanagement of personal data.

      2.  Small companies are the leading edge on AI. Nimbler than their
            larger rivals, small businesses in the app economy have a
            comparative advantage in developing, deploying, and adapting AI
            tools for a variety of purposes. Their experience is a primary
            shaping force in the development of FM services and generative
            AI in all of its various forms and should play a major role in
            informing policymakers on how any new laws should apply to AI's
            development and use. They also benefit from a competitive
            landscape in markets for LM and FM services. They do not want
            policymakers to prematurely intervene on antitrust grounds, a
            development that could ironically stifle competition on the
            features small businesses care about most.

      3.  Standards are going to play an important role in AI and other
            emerging technologies. Small businesses leverage standards
            every day to compete with larger rivals and to interoperate
            with products and services that they can build on. The National
            Institute for Standards and Technology (NIST) plays an
            important role as a participant in voluntary, industry-led
            standards development efforts, as a clearinghouse of
            information on standards development, and as a coordinator of
            Federal government participation in industry-led processes.
            However, Congress must encourage NIST's leadership in
            preserving small businesses' access to standardized
            technologies by holding standard-essential patent (SEP) holders
            to their commitments to license SEPs to any willing licensee on
            fair, reasonable, and non-discriminatory (FRAND) terms.
    II. A Federal Privacy Framework
        The proliferation of AI tools across industries and around the
    world is one of the most important reasons for Congress to establish a
    single, preemptive Federal privacy framework. While a restrained
    approach to governance should guide Congress' thinking on AI as a group
    of technologies, Congress has a more developed understanding of the
    privacy questions at issue with their adoption. With 19 state-level
    comprehensive privacy frameworks in place, Europe's General Data
    Protection Regulation (GDPR), and a host of other regulatory regimes in
    various stages of consideration and implementation, privacy is a good
    fit for congressional action.
        The privacy risks AI poses are outgrowths of existing privacy
    issues. Privacy is concerned first and foremost with the universe of
    purportedly authorized collection, processing, and transfer activities
    an entity may pursue. The impracticability of a single consumer
    understanding and authorizing all the foreseeable uses of data across
    all of the services they access--coupled with the elusiveness of
    defining privacy harms--have long presented formidable challenges for
    policymakers approaching the privacy problem. With AI, these same
    challenges emerge, but on a larger scale and with greater intensity on
    the foreseeability factor. For small businesses, the twin imperatives
    to provide regulatory clarity and to avoid taking away the tools they
    use now are heightened. With these considerations in mind, it may help
    to describe small businesses' priorities for federal privacy
    legislation, with a special focus on AI and with the American Privacy
    Rights Act (APRA) as a reference.
        Data minimization. AI is often useful precisely because it surfaces
    insights or ideas that people are unable or less inclined to find on
    their own. Therefore, the purpose the data serves, for both the entity
    processing it and the individual to which it pertains, often evolves
    after its initial collection. For small businesses, a data minimization
    provision that imposes a blanket ban on processing of personal data,
    except in specifically enumerated circumstances, is likely to cause
    problems. Both GDPR and APRA impose such a ban on processing, unless
    specific lawful bases exist. GDPR's formulation is more permissive, as
    it allows processing that is ``necessary for the purpose of the
    legitimate interests pursued by the controller or by a third party''
    and where the ``data subject has given consent to the processing'' for
    ``specific purposes.'' \2\ In general, data minimization provisions
    should take the opposite approach, avoiding a blanket ban on all
    processing while describing prohibited processing activities that are
    likely to cause net concrete harm. Those concrete harms are not
    necessarily limited to financial or physical harms; then-Acting Chair
    of the FTC Maureen Ohlhausen's useful 2017 taxonomy of recognized harms
    outside those categories, including reputational injury and unwarranted
    intrusion, is still applicable.\3\ Consistent with these concepts, if
    Congress proceeds with proscriptions on processing activities, we
    recommend the use of relevant context and consumers' reasonable
    expectations as twin touchstones. The extent to which processing of
    personal data respects the reasonable expectations of consumers given
    the context in which data processing takes place is often the best
    predictor of possible risk of privacy harms occurring.
    ---------------------------------------------------------------------------
        \2\ Gen. Data Protection Reg., Art. 6, available at https://gdpr-
    info.eu/art-6-gdpr/.
        \3\ Hon. Maureen K. Ohlhausen, Acting Chair, Fed. Trade Comm.,
    ``Painting the Privacy Landscape: Informational Injury in FTC Privacy
    and Data Security Cases,'' speech before the Fed. Comm'cns Bar Assoc.
    (Sept. 19, 2017), available at https://www.ftc.gov/system/files/
    documents/public_statements/1255113/privacy_speech_mkohlhausen.pdf.
    ---------------------------------------------------------------------------
        Explicit small business treatment. In legislatures across the
    country, proponents of privacy measures have indicated a desire to
    ensure small businesses are able to comply with and are not unduly
    burdened by any applicable requirements. However, we believe that
    APRA's specific method of achieving that goal--carving out small
    businesses from the definition of ``covered entity''--could potentially
    deny them the benefits of preemption and inadvertently expose them to
    costly state-by-state compliance and unreasonably high litigation risks
    from differing liability regimes.
        By excluding small businesses from the definition of covered
    entity, APRA would create uncertainties as to whether small businesses
    would be covered by the bill's preemption provision--or if, instead,
    they would remain regulated by existing and future state privacy laws.
    If small businesses are not covered entities under APRA's preemption
    provision, which preempts state laws ``covered by'' the Federal law,
    they may have to contend with a burdensome patchwork of state privacy
    laws while their larger counterparts enjoy a single privacy standard.
    If the Committee is worried about further entrenching larger companies
    with big compliance budgets while disadvantaging smaller companies, we
    believe a pure carveout would have exactly this effect.
        As we have previously indicated in testimony, App Association
    members are not asking to be carved out of Federal data privacy
    legislation.\4\ Instead, requirements should be scalable depending on
    the size, nature, and complexity of an enterprise's processing
    activities, and small businesses should have access to compliance
    programs that provide a presumption of compliance with the law. We note
    that some of the world's largest companies have had to make prodigious
    compliance investments to meet GDPR requirements that are simply beyond
    the reach of App Association members. For example, Google's chief
    privacy officer testified before this Committee in 2018 that the
    company had to invest ``hundreds of years of human time'' to come into
    compliance with the framework.\5\ Offering small businesses a path to
    compliance is essential to ensuring the law holds them accountable, but
    that they have some protection from nuisance lawsuits and are afforded
    reasonable opportunities to rectify compliance issues in good faith. A
    compliance program would ensure that App Association members are
    rightfully viewed as--and held accountable for--complying with a
    Federal framework, while alleviating excessive liability concerns and
    other burdens.
    ---------------------------------------------------------------------------
        \4\ ``Protecting America's Consumers: Bipartisan Legislation to
    Strengthen Data Privacy and Security,'' hearing before the House
    Committee on Energy and Commerce, Subcommittee on Consumer Protection
    and Commerce, (117th Cong., 2d Sess.), statement of Graham Dufault,
    senior dir. for public policy, ACT | The App Association, available at
    https://docs.house.gov/meetings/IF/IF17/20220614/114880/HHRG-117-IF17-
    Wstate-DufaultG-20220614.pdf (``The safe harbor would ensure that App
    Association members are rightfully viewed as--and held accountable
    for--complying with a Federal framework. . .'').
        \5\ Ashley Rodriguez, ``Google says it spent ``hundreds of years of
    human time'' complying with Europe's privacy rules,'' Quartz (Sept. 26,
    2018), available at https://qz.com/1403080/google-spent-hundreds-of-
    years-of-human-time-complying-with-
    gdpr#::text=Google's%20chief%20privacy
    %20officer%2C%20Keith,company%20into%20compliance%20with%20GDPR.
    ---------------------------------------------------------------------------
        Preemption. Preemption must be strong and without vague exceptions.
    The great economic benefit of the Internet is that it has allowed small
    businesses to reach customers in all 50 states. Small businesses do
    not, however, have the time or capacity to conduct constant legal
    analyses to determine whether relevant state law or Federal law applies
    to their activities with each new scenario presented. Therefore, a
    preemption provision should expressly preempt state laws ``related to''
    the Federal law and should not have exceptions that call into question
    Congress' intent with respect to preemption.
        Private right of action (PRA). A Federal privacy framework must
    avoid inadvertently creating new sue-and-settle business models and
    inviting other abusive tactics based on claims with no merit. We
    acknowledge that the difficulty in bringing suit under current law lies
    in defining the harm that accrues to the aggrieved consumer. But, even
    under a new Federal regime, in the majority of cases, enforcement
    agencies are equipped to obtain redress to the extent it Is warranted.
    Therefore, we do not believe a PRA is a necessary element of a
    comprehensive Federal privacy framework. However, if a bipartisan
    Federal privacy bill includes a PRA, several safeguards are necessary:
    remedies must not include statutory per-person, per-violation monetary
    damages; any violation of the law should not constitute an injury-in-
    fact; businesses must be allowed an opportunity to cure the alleged
    problem before a suit is allowed to proceed; there must be penalties
    for baseless claims; and Congress should require notice to Federal or
    state enforcement agencies, empowering them to veto baseless claims.
    III. How Small App Companies and Connected Device Makers Use, Develop,
            and Adapt AI Tools
        The reality of AI use in everyday marketplaces is both less
    shocking and more interesting than headlines and handwringers suggest.
    For example, focusing on the most egregious instances of facial
    recognition misuse by law enforcement agencies would provide an
    inadequate basis for understanding how AI works in the vast majority of
    commercial cases and the risks it truly presents. An overview of how
    App Association members use AI in diverse ways may provide a more
    helpful and representative sample of how AI benefits people and the
    kinds of risks at issue. For a lengthier report on our member
    companies' perspectives on and uses of AI, our white paper, Small
    Businesses and Entrepreneurs: An Indispensable Force in the AI App
    Economy, describes further examples from members in the United States
    and overseas.\6\
    ---------------------------------------------------------------------------
        \6\ ACT | The App Association, Small Businesses and Entrepreneurs:
    An Indispensable Force in the App Economy (Fall 2023), available at
    https://actonline.org/wp-content/uploads/small-businesses-and-
    entrepreneurs-an-indispensable-force-in-the-ai-app-economy.pdf.
    ---------------------------------------------------------------------------
        In a recent App Association member survey, 75 percent reported
    using generative AI.\7\ The figure for all kinds of AI is likely close
    to 100 percent, and since that survey was conducted late last year, the
    number using generative AI has likely increased. Many App Association
    members are also on the developer side of AI tools, and some even make
    the connective tissue for developers to customize LM resources for
    specific purposes. Among a range of enterprises, another survey
    recently revealed that among those that deploy large LMs, only 3
    percent use just one, while 83 percent use three or more.\8\
    Summarizing the reason for this variety, one of the respondents said,
    ``[f]or about 50 percent of use cases, we use OpenAI, for 30 percent--
    40 percent of use cases, we use our internal fine-tuned and pre-trained
    LLMs based on open-source LLMs like Llama 2 or Mistral, and for others
    we use Anthropic or Cohere.'' \9\ Looking at the broader marketplace,
    the internal fine-tuned segment is especially common and an area where
    App Association members play a significant role, as they and their
    clients use AI to help them be more efficient at what they do not
    necessarily do that well so that they can focus on what they do best.
    As generative AI's lifecycle transitions from hype to rubber meeting
    road, these smaller models are emerging as the more realistic immediate
    use case for enterprises around the world.\10\ For their part, small
    businesses say they are motivated to adopt AI tools primarily for cost
    savings and due to competitive pressure, and the most common uses by
    small businesses for AI tools are financial management, e-mail
    marketing automation, and to enhance cybersecurity capabilities.\11\
    ---------------------------------------------------------------------------
        \7\ Priya Nair, ``Survey Says: AI and IP are Essential to
    Innovation,'' ACT | The App Association Blog (Feb. 27, 2024), available
    at https://actonline.org/2024/02/27/survey-says-ai-and-ip-are-
    essential-to-innovation/.
        \8\ CBCInsights, Status: Open Relationship, available at https://
    tinyurl.com/58y7bezu.
        \9\ Id.
        \10\ Tom Dotan and Deepa Seetharaman, ``For AI Giants, Smaller Is
    Sometimes Better,'' The Wall Street J. (Jul. 6, 2024), available at
    https://www.wsj.com/tech/ai/for-ai-giants-smaller-is-sometimes-better-
    ef07eb98.
        \11\ SBE Council, Small Bus. AI Adoption Survey, (Oct. 2023),
    available at https://sbecoun
    cil.org/wp-content/uploads/2023/10/SBE-Small-Business-AI-Survey-Oct-
    2023-FINAL.pdf.
    ---------------------------------------------------------------------------
        Decision support: healthcare. One of our member companies recently
    built a simple, AI-driven tool that both expands options for small
    healthcare practices and helps invigorate competition in a key market
    for practice management systems (PMS). These systems help healthcare
    practices manage their patient intake processes and are especially
    important for scheduling and managing contact and communications with
    patients. It can be especially difficult for small practices to find
    the right PMS that fits their specialty or patient population. Our
    member built the tool to rapidly scan and analyze broader range of PMS
    options on the market and rank them in terms of which would be the best
    fit for the client practice. It is an excellent example of AI made by
    small businesses for small businesses, helping solve a targeted problem
    and providing a custom solution in a manner that improves efficiency.
        Caregivers have used clinical decision support (CDS) tools to help
    accurately diagnose conditions and build treatment plans that best fit
    their patients. App Association member Rimidi provides a remote
    physiologic monitoring and clinical decision support platform for
    patients and their providers to manage a variety of chronic
    conditions.\12\ Rimidi helps ensure that providers can intervene when
    necessary when a patient's diabetes is at risk of reaching uncontrolled
    levels and helps chart a management plan that fits unique patients and
    their symptoms.
    ---------------------------------------------------------------------------
        \12\ Rimidi, available at https://rimidi.com.
    ---------------------------------------------------------------------------
        Generative AI. Real-world LM deployment is typically not a case of
    workers punching a prompt into ChatGPT and copying and pasting the
    results. For example, App Association members adapt LMs to draft
    marketing materials and write code. They start with either licensed or
    open-source models and fine tune them to produce results that better
    fit the results they want. Fine-tuning can also help an organization
    ensure a LM's outputs hew more closely to ethical and legal
    requirements, reducing the editing and review burden on the people in
    the loop.
        In another example, App Association member Rotational Labs creates
    custom plug-ins for their clients to maximize their use of available
    LMs. They also help create ``domain-specific'' LMs using open-source
    models, allowing their clients to create their own models with their
    own data, either on a provided cloud infrastructure or the client's
    existing service. For one client, Rotational built a custom domain-
    specific model that successfully reduced manual review of tens of
    thousands of news articles by 90 percent.\13\
    ---------------------------------------------------------------------------
        \13\ Rotational Labs, Case Study: BlueVoyant, available at https://
    rotational.io/case-studies/blue-voyant/.
    ---------------------------------------------------------------------------
        Decision support: financial services. Common in industries from
    fast food to healthcare, AI decision support tools have reached
    virtually every part of the economy. App Association members are
    redefining productivity by building and servicing decision support
    options driven by AI. For example, Florida (and Washington)-based
    Devscale built Clockwork.ai, a data visualization and decision support
    platform for financial professionals.\14\ The tool integrates with
    commonly used bookkeeping software to provide projections and ``crunch
    numbers'' while their clients can focus on relationships and expanding
    their business. Clockwork is, in turn, able to save its clients over 10
    hours per month and help them grow their enterprises by 50 percent.\15\
    This is an example of a deployment that does not draw on large LMs or
    FMs, but still uses machine learning to generate recommendations for
    users.
    ---------------------------------------------------------------------------
        \14\ Devscale, Case Study, Clockwork, available at https://
    devscale.com/casestudy/clockwork/.
        \15\ Clockwork, available at https://www.clockwork.ai/.
    ---------------------------------------------------------------------------
        Decision support: pizza quality. Other kinds of decision support
    tools have been around for longer and are developed in-house. Domino's
    Pizza began training their DOM Pizza Checker in 2019, inviting
    customers to take pictures of their pizza in exchange for redeemable
    points.\16\ Domino's trained DOM with 5,000 images of pizza on an
    NVIDIA DGX platform and deployed it in 2021. The system captured pizzas
    as they left the oven, assessing each for pizza type, correct toppings,
    topping distribution, and aesthetic appeal. The system can flag a
    finished pizza that is likely to generate a consumer complaint,
    enabling store management to quickly identify and address quality
    issues as they arise. Domino's says the quality of their pizza has
    improved 14 to 15 percent in the stores that have deployed DOM.
    ---------------------------------------------------------------------------
        \16\ Heili Palo, ``The Secret Surveillance Sauce to Domino's
    Pizza's Success,'' Assignment: Competing with Data, Digital Innovation
    and Transformation, Harvard Bus. School (Mar. 28, 2022), available at
    https://d3.harvard.edu/platform-digit/submission/the-secret-
    surveillance-sauce-to-dominos-success/.
    ---------------------------------------------------------------------------
        Decision support: smart agriculture. App Association members are
    also modernizing agriculture with software, smart devices, and AI. For
    example, Honolulu-based Smart Yields connects farmers and agricultural
    researchers to increase crop yield, revenue, and productivity. In a
    case study conducted with the Kohala Institute, Smart Yields embedded
    their sensors into the water system and soil around different crop
    fields, allowing for maximized water usage, better understanding of
    erosion and soil management, and an overall reduction in agricultural
    waste. Their use of retrofitted monitoring collars on local pig
    populations and a related algorithm that helps to monitor and predict
    herd movements has allowed farmers in the area to better protect their
    crop, especially macadamia nuts, while still preserving the land and
    safety of these native animals critical to the overall health of the
    local ecosystem. Smart Yields is helping Hawaii meet their commitment
    to doubling food production by 2030 and other communities achieve
    similar goals around the world.
        Competition in the markets for LM and FM services. Taking stock of
    how small businesses use AI in practice, they tend to select the
    options that fit their specific purposes. As a result, they often rely
    on a combination of services and customizations of the raw models.
    Conceptions of the marketplace as dominated by a handful of firms
    behaving anticompetitively do not comport with their reality. While
    some commenters have cast the burgeoning market for LM services as
    locked in by large companies, the truth is that the future of the
    market for LM services is far from clear. Moreover, the market for AI
    services generally is robust, with plenty of competitors entering and
    competing successfully in the market.\17\ These commenters' premises
    suggest Congress and other officials should do something to discourage
    or stop the participation of companies over a certain size in these
    markets. But small businesses stand to lose the most should artificial
    barriers to entry, such as imposing a licensing regime for FMs, be
    created in such policies. Small businesses also likely derive the most
    benefit from the creation of vertically integrated distribution and
    service offerings these companies are best positioned to provide. Ex
    ante regulations or enforcement actions designed to require strict
    interoperability or open access to LM services could also serve to
    eliminate the aspects of these services on which LM and cloud providers
    currently compete vigorously for App Association members' business,
    including privacy and security.\18\
    ---------------------------------------------------------------------------
        \17\ See Kendrick Kai, ``AI50,'' Forbes (Apr. 11, 2024), available
    at https://www.forbes.com/lists/ai50/.
        \18\ See Kedhar Sankararaman, ``Unlocking the Future: How Smart
    Investments and Sensible Governance Can Propel the LLM Industry
    Forward,'' ACT | The App Association Blog (Jun. 24, 2024), available at
    https://actonline.org/2024/06/24/unlocking-the-future-how-smart-
    investments-and-sensible-governance-can-propel-the-llm-industry-
    forward/.
    ---------------------------------------------------------------------------
        Small businesses in the app economy are skeptical of government
    discouraging the investment of billions of dollars to create the
    infrastructure on which they plan to build their problem-solving
    innovations. Seemingly laying the groundwork for premature enforcement
    activity, the FTC's 6b study on generative AI markets\19\ is concerning
    evidence of an intent to interfere with or control the extent to which
    small businesses are able to do business with their largest partners
    and clients. Small companies have invested and will continue to invest
    in creating LMs, but limiting the size of the companies with which they
    can transact for services that require significant investment is not
    fair to the small businesses that need those offerings to be robust.
    Similarly, small businesses do not want policymakers to forbid them
    from partnering with their most lucrative clients and partners that
    happen to have the most powerful distribution channels. Thus, the
    government should not prevent or discourage larger companies with
    existing physical assets or know-how from moving vertically into FM and
    LM services.
    ---------------------------------------------------------------------------
        \19\ Fed. Trade Comm'n, press release, ``FTC Launches Inquiry into
    Generative AI Investments and Partnerships,'' (Jan. 25, 2024),
    available at https://www.ftc.gov/news-events/news/press-releases/2024/
    01/ftc-launches-inquiry-generative-ai-investments-partnerships.
    ---------------------------------------------------------------------------
        Consider a parallel example. As the latest model cars take on more
    autonomous features, the industry is undergoing major shifts. New
    entrants are taking advantage of these disruptions to make credible
    inroads, while incumbent auto manufacturers are poised to leverage
    their existing vertical supply chains and manufacturing bases to make
    autonomous cars. Imagine if policymakers decided incumbent auto
    manufacturers should either be barred from making autonomous vehicles
    or regulated differently from market entrants just because they already
    have a vertically integrated advantage and compete successfully in the
    market for non-autonomous cars. Such a decision would deprive consumers
    of options from some of the strongest competitors and artificially box
    out years of experience in areas like safety.
        Similarly, companies like Amazon, Microsoft, and Google have a
    sprawling network of existing assets in upstream industries such as
    cloud computing, which already serve as part of the vertical stack for
    LLMs and FMs. Just like building a car requires auto manufacturing
    capacity and a supply chain, LMs and FMs require significant computing
    power, and this is one of the main inputs for LM and FM services. In
    turn, cloud services are the main providers of this capacity. So,
    government intervention to prevent cloud companies from providing cloud
    capacity for their own FM efforts--or even to prevent them from
    providing compute capacity to other FM or LM projects--just because
    they are large incumbents in cloud computing appears to defeat the
    purpose of antitrust law.
    IV. Standards are Critical for AI Technology Development
        As technical standards are integrated into AI products and as AI
    standards form, a fair and balanced standards ecosystem will drive U.S.
    AI-based invention in critical sectors, including green technology and
    precision agriculture. We appreciate Congress' focus on this issue, as
    several members of this Committee have introduced measures aimed at
    enhancing American leadership in supporting the development and
    adoption of critical and emerging technology (CET) standards, which
    are--and will be--an important support of transformational AI-driven
    technologies around the world. Voluntary, open, private sector-led
    standards development has been a decisively winning approach for
    American technology interests. Any legislation to support this
    ecosystem must balance the interests of advancing American leadership
    with maintaining the openness of standards development organizations
    and the primacy of private sector leadership.
        Among the measures introduced, we support the bipartisan Promoting
    United States Leadership in Standards Act (S. 3849), led by Senators
    Mark Warner and Marsha Blackburn.\20\ We believe this legislation can
    better position standards development organizations and standards
    participants for success. A strong, yet nimble, approach to technical
    standards development is a foundational imperative for the App
    Association's members as they create tomorrow's innovations. Nurturing
    open and global participation in standardization activities, especially
    when hosted in the United States, can address shared technical
    challenges while advancing American technology leadership. If the
    Committee considers this legislation, we would like the authors to
    consider removing ``prestandardization'' and ``standards coordination''
    from the activities supported by the funds that would be allocated for
    the grant program. NIST already undertakes these activities, as do
    private sector-led standards development organizations (SDOs). Thus,
    although NIST should have more resources, funds set aside for hosting
    meetings in the United States should not also be set aside for
    activities NIST is already undertaking.
    ---------------------------------------------------------------------------
        \20\ Promoting U.S. Leadership in Standards Act (S. 3849, 118th
    Cong.), available at https://
    www.congress.gov/bill/118th-congress/senate-bill/
    3849?q=%7B%22search%22%3A%22s+3849%
    22%7D&s=1&r=1.
    ---------------------------------------------------------------------------
        With respect to the array of additional proposals and oversight
    issues affecting NIST and its role in standards development, we offer a
    couple of guiding principles:

        NIST should remain a supporter, rather than arbiter, of
    international standards development. Any legislation must avoid
    unintentionally recasting NIST's long-standing role in standards
    development. Legislation should not task NIST with evaluating the merit
    of standards developed by SDOs or auditors checking on testing,
    evaluation, verification, or validation.
        International collaboration. Legislation should avoid creating an
    appearance of convening a ``voting bloc'' that could cause the creation
    of competing blocs and lead to global fragmentation of standards
    development.
        Standard-essential patent abuse. While the United States is the
    world leader in technology innovation, China is expanding its attacks,
    including through anticompetitive standard-essential patent (SEP)
    licensing abuses, which undermine the functioning of the open standards
    system. Originating in the telecommunications sector but now seen
    across key U.S. economic verticals, SEP abuse continues to be enabled
    by a lack of support and enforcement on the U.S. government's part,
    which in turn continues to prompt bad faith SEP licensor practices. In
    one common example of SEP abuse, certain SEP licensors have been known
    to commit to making SEP licenses available to any licensee on FRAND
    terms and then breaking those commitments by systematically seeking
    injunctions against willing and reasonable licensees. In another
    example, certain SEP licensors refuse to license their SEPs to
    innovators at the most appropriate point in a supply chain, instead
    only making their licenses available to downstream manufacturers,
    unilaterally deciding for entire value chains where licenses can and
    cannot be taken. Conduct like this demonstrably increases costs for
    consumers, wastes vast amounts of capital on litigation instead of
    innovation, and pulls the rug out from under small businesses that rely
    on being able to take a license in order to interoperate with CET
    standards.
        Federal policy must combat this abuse by holding bad actor SEP
    licensors to their commitments to license to any willing licensee--
    including small businesses--on FRAND terms. NIST was previously a
    signatory on a Federal policy statement on SEP licensing that,
    unfortunately, mischaracterized Federal law as allowing SEP injunctions
    against willing licensees. Having withdrawn the statement, the Biden
    Administration has not issued a new one, but we believe the Executive
    Branch should adopt a new policy that accurately describes the
    interplay of standards, patent licensing, and competition laws, and
    provide certainty and clarity to the ecosystem that:

       The FRAND Commitment Means All Can License. A holder of a
            FRAND-committed SEP must license that SEP to all companies,
            organizations, and individuals who use or wish to use the
            standard on FRAND terms.

       Prohibitive Orders on FRAND-Committed SEPs Should Only Be
            Allowed in Rare Circumstances. Prohibitive orders (federal
            district court injunctions and U.S. International Trade
            Commission exclusion orders) should not be sought by SEP
            holders or allowed for FRAND-committed SEPs except in rare
            circumstances where monetary remedies are not available.

       FRAND Royalties. A reasonable rate for a valid, infringed,
            and enforceable FRAND-committed SEP should be based on the
            value of the actual patented invention itself, which is
            separate from purported value due to its inclusion in the
            standard, hypothetical uses downstream from the smallest
            saleable patent practicing unit, or other factors unrelated to
            invention's value.

       FRAND-committed SEPs Should Respect Patent Territoriality.
            Patents are creatures of domestic law, and national courts
            should respect the jurisdiction of foreign patent laws to avoid
            overreach with respect to SEP remedies. Absent agreement by
            both parties, no court should impose global licensing terms on
            pain of a national injunction.

       The FRAND Commitment Prohibits Harmful Tying Practices.
            While some licensees may wish to get broader licenses, a SEP
            holder that has made a FRAND commitment cannot require
            licensees to take or grant licenses to other patents not
            essential to the standard, invalid, unenforceable, and/or not
            infringed.

       The FRAND Commitment Follows the Transfer of a SEP. As many
            jurisdictions have recognized, if a FRAND-committed SEP is
            transferred, the FRAND commitments follow the SEP in that and
            all subsequent transfers.

        To advance the Congress' laudable goals of solidifying American
    leadership and elevating small businesses' freedom to innovate, strong
    enforcement against well-demonstrated SEP abuses is necessary. As
    foreign courts diverge on whether and to what extent to respect U.S.
    domestic law as it applies to SEP remedies, the Federal government
    cannot afford to relinquish its long-standing leadership position on
    SEP issues. Our own national interests would be best served by taking a
    strong stand against SEP abuse, particularly around CET standards, and
    we believe the Administration's National Standards Strategy for
    Critical and Emerging Technologies (NSSCET) implementation offers prime
    opportunities to establish this leadership.
    V. Conclusion
        Small businesses are leading the way in defining AI's beneficial
    uses and developing ways of managing and avoiding the risks presented.
    With their comparative advantage in speed and agility, the experiences
    and perspectives of small app companies and connected device makers
    provide a realistic picture of how AI is used in everyday commerce. We
    deeply appreciate that this Committee considers these perspectives as
    part of its inquiry into the risks and benefits of AI and how Federal
    privacy law may affect them. We look forward to working with the
    Committee to ensure that small businesses can continue to innovate on a
    strong foundation of privacy protection, standards access and
    development, and a free market with ample opportunities.
                                     ______

                                   Appendix A
    Majority Members
    Maria Cantwell, Washington (Chair)
        Headquartered in Seattle and founded in 2008, Digital World Biology
    creates digital educational tools to help students learn modern
    biology. Their app, Molecule World, is an easy-to-use visualization of
    3D molecular structures ready for classroom use upon download. They
    also have a variety of textbook-like materials that help students learn
    quickly with visual aids and assist teachers with keeping students
    engaged with hands-on activities throughout the chapters.
    Amy Klobuchar, Minnesota
        Located in the Twin Cities and founded in 2013, Vemos is a platform
    solution for bars, restaurants, and other venues as a one-stop-shop for
    the digital tools needed to manage and grow their businesses. Operating
    with only nine full-time employees, Vemos found a way to harness and
    present a venue's data in a humanized way, which helps venues
    understand who their customers are and how to market to them
    effectively.
    Brian Schatz, Hawaii
        Founded in 2015 and headquartered in Honolulu, Smart Yields is an
    intelligent agriculture software that connects farmers and agricultural
    researchers to actionable real-time data, allowing them to increase
    crop yield, revenue, and productivity. With fewer than 10 employees,
    Smart Yields is committed to helping Hawaii meet its commitment to
    doubling food production by 2030 and other communities achieve similar
    goals around the world.
    Ed Markey, Massachusetts
        Established at the Massachusetts Institute of Technology (MIT) in
    2011, Podimetrics is a medical technology services company that
    develops hardware-enabled, thermal-imaging solutions to predict and
    prevent diabetic foot ulcers. The Podimetrics
    SmartMatTM monitors the temperature of diabetes patients'
    feet to identify temperature asymmetries that signal the development of
    a foot ulcer. Coupled with a monitoring service, the Podimetrics Remote
    Temperature Monitoring SystemTM uses the wireless
    SmartMatTM to notify patients and clinicians of temperature
    asymmetry and inflammation, the first signs of foot ulcers, preventing
    amputations and other health complications.
    Gary Peters, Michigan
        With their U.S. operations based near Lansing, Michigan, Payeye has
    developed a unique eye-based payment method that combines hundreds of
    biometric data points within the iris and face to verify a customer's
    identity. They also offer express e-payments for e-commerce via QR
    codes and have developed a specific marketing and sales ecosystem
    centered around their technology.
    Tammy Baldwin, Wisconsin
        Based in Onalaska, Sergeant Laboratories is a software company that
    builds advanced IT security and regulatory compliance products for
    businesses. Their flagship product, AristotleInsight, measures and
    quantifies risk management data and provides detailed reports to
    compliance and security professionals to make informed decisions and
    stay a step ahead of cybercriminals.
    Tammy Duckworth, Illinois
        Aggieland Software is an IT consulting and custom software
    development company located in Springfield. The team at Aggieland
    Software helps their clients achieve sustainable growth and efficiency
    through services involving blockchain, artificial intelligence, and
    software development.
    Jon Tester, Montana
        Headquartered in Bozeman, Guidefitter is an online and mobile
    platform that connects people with guides, nature experts, and
    sportspersons for safe and guided natural expeditions and sports,
    including hunting, fishing, hiking, and camping. The platform also
    allows the experts to promote their business or experience and
    facilitates payment for merchandise as well as the guided tour or
    event.
    Kyrsten Sinema, Arizona
        Founded in 2019, LiteraSeed is a digital health startup creating a
    visual way for patients to share their symptoms with their doctors. The
    product, called a ``visual symptom report,'' focuses on helping
    patients whose first language is not English and those with lower
    literacy levels to communicate with their doctors and better understand
    their medical records.
    Jacky Rosen, Nevada
        Pigeonly is an online and mobile platform that connects inmates
    with their loved ones. Their services provide a central place to send
    letters, pictures, cards, and more. Through the platform, families can
    also call their inmate at a lower cost and stay in touch throughout
    their incarceration. The company's mission is to improve communication
    and community for those incarcerated and to encourage families to stay
    in touch with their inmates by simplifying and streamlining the
    process.
    Ben Ray Lujan, New Mexico
        Snowball is an all-in-one fundraising platform that connects users
    with more than 15,000 nonprofits nationwide. The app has two parts. The
    first is for donors, giving them information about the nonprofits in
    Snowball's network, donation opportunities, and notice of emergency
    relief needs. It also provides a secure place to track donations and
    save credit card information. The second, for nonprofits, helps to keep
    track of donors, grow their donor base, and communicate fundraising
    opportunities.
    John Hickenlooper, Colorado
        Founded in 2007, Alchemy Security is a boutique cybersecurity firm
    based in the Central Rockies of Colorado. They serve as the
    cybersecurity expert for their clients, helping them make informed
    business decisions on how and where to invest valuable resources to
    minimize information security risks. Through a combination of risk
    analysis, security information and event management (SIEM), and other
    market-available technologies, Alchemy Security tailors their solutions
    to address the unique needs of their customers, regardless of industry
    or sector.
    Raphael Warnock, Georgia
        Based in Atlanta, Georgia, Rimidi creates mobile apps and software
    focused on supporting clinicians in the development of remote patient
    monitoring and chronic care management programs. Their clinical
    decision support tools, which work directly within existing electronic
    health records (EHR), combine patient-generated health data with
    clinical data allowing providers to make better-informed treatment and
    management decisions while also improving patient-engagement throughout
    care.
    Peter Welch, Vermont
        Aprexis Health Solutions is a cloud-based software that helps
    patients with personalized services for Medication Therapy Management
    and includes more than 1,000 participating pharmacies and more than one
    million patients. Founded in 2009, Aprexis works with health plans,
    pharmacy networks, corporate employers, and providers to deliver
    improved, patient-centric health outcomes.
    Minority Members
    Ted Cruz, Texas (Ranking Member)
        Founded in 2018 by a trained speech pathologist, For All Abilities
    uses data to help employers amplify their employees' strengths while
    supporting their weaknesses through ADA/Disability 101 training and
    support throughout Equal Employment Opportunity Commission violation
    audits. Their main mission is to increase inclusion and equity for
    people with disabilities and help employers embrace the different
    abilities of employees.
    John Thune, South Dakota
        Infotech Solutions, LLC, is a concierge IT service helping
    businesses with everything from implementing a new software system or
    network to maintenance, general IT issues, security, and more. The
    company also offers an app across platforms that helps their clients
    troubleshoot IT issues and connect with their IT service team remotely.
    Roger Wicker, Mississippi
        Alpha Victoria Studios, founded in 2016, is based in Gulfport and
    boasts a team of three creative engineers. This IT contracting business
    provides their clients with web and mobile software development,
    digital marketing, and search engine optimization services.
    Deb Fischer, Nebraska
        LyncStream, founded in 2012 in western Omaha, helps businesses of
    every size and industry use technology to grow and automate their
    business. They provide a litany of services, including database
    technology, web and mobile software development, and product management
    throughout the software lifecycle.
    Jerry Moran, Kansas
        Founded in 2014, Foster Care Technologies is an evidence-based
    support tool that helps inform placement decisions in foster care.
    Through their work with the University of Kansas School of Social
    Welfare, it was determined their product leads to better long-term
    placement outcomes for children in foster care, as well as reduced
    costs for agencies working on placement.
    Dan Sullivan, Alaska
        StepAway is a mobile application to help those with addiction
    manage their day-to-day and make better decisions about their daily
    habits to help prevent relapses. The app is primarily centered around
    those who are unable to seek addiction treatment services but are
    looking to make a change in their drinking habits. The app helps track
    daily progress while also giving users insight into their triggers and
    provides valuable information on how to make different and better
    decisions related to their alcohol use in a safe and private space.
    Marsha Blackburn, Tennessee
        Based in Nashville, Acklen Avenue provides clients with fully
    formed and outsourced software development teams. Founded in 2011,
    their services are tailored to each client's projects and staffing
    needs, making development a quick and efficient process whether the
    client is looking to develop a new aspect of an existing project launch
    or update.
    Todd Young, Indiana
        Located in Fishers, Arborgold is a software company helping
    landscaping businesses become more efficient and profitable since 1994.
    Their software encompasses job scheduling, resource management, profit
    margin estimations, and a series of mobile apps for employees to seek
    help regarding estimates, work orders, and other essential information.
    Ted Budd, North Carolina
        Founded in 1994 and headquartered in Chapel Hill with 26
    employees,/n Software provides clients with the tools they need to
    build internet-enabled web and desktop applications. Software
    developers at most Fortune 500 companies use their flagship product,
    IPWorks, to build their connected applications.
    Eric Schmitt, Missouri
        Founded in 2012 in Joplin, Midwestern Interactive provides their
    clients with embedded teams of experts. The team of nearly 100 assists
    clients with strategic planning around software, branding, and digital
    content. In addition, the team at Midwestern Interactive provides their
    clients with additional on-the-ground support as they implement their
    digital strategies.
    J. D. Vance, Ohio
        Located in Cincinnati since their founding in 2016, Canned Spinach
    is a custom software development company that helps companies of all
    sizes make a big impact. From some of the smallest startups to some of
    the largest Fortune 500 companies, Canned Spinach helps businesses
    bring their ideas into reality by launching new products and services.
    They provide clients with web and mobile software development,
    including design and next-generation technology, such as augmented
    reality experiences.
    Shelley Moore Capito, West Virginia
        TMC Technologies is an IT services company focused on helping their
    clients, both Federal and local, with program and project management,
    scalable system and software engineering, IT infrastructure design and
    management, and network and telecom services. TMC Technologies has
    focused a lot of their IT work in their own backyard, providing IT
    services for West Virginia companies, especially small business owners
    looking to bring their company into the digital age.
    Cynthia Lummis, Wyoming
        BlackFog is a cyberthreat prevention company that uses a unique
    combination of behavioral analysis and data exfiltration technology to
    identify, stop, and prevent future data hacks, unauthorized data
    collection, and more across mobile and web endpoints. Their services
    protect their clients and their clients' most sensitive data and
    privacy while also strengthening their regulatory compliance.
                                     ______

                                   Appendix B
    General Views of the App Association on Advancing Governance,
            Innovation, and Risk Management for Agency Use of Artificial
            Intelligence
        The App Association represents small business innovators and
    startups in the software development and high-tech space located across
    the globe.\21\ As the world embraces mobile technologies, our members
    create the innovative products and services that drive the global
    digital economy by improving workplace productivity, accelerating
    academic achievement, and helping people lead more efficient and
    healthier lives. Today, that digital economy is worth more than $1.8
    trillion annually and provides over 6.1 million American jobs.\22\ App
    Association members create innovative software and hardware technology
    solutions and are at the forefront of incorporating artificial
    intelligence (AI) into their products and processes.
    ---------------------------------------------------------------------------
        \21\ ACT | The App Association, About, available at http://
    actonline.org/about.
        \22\ ACT | The App Association, State of the U.S. App Economy:
    2023, https://actonline.org/wp-content/uploads/APP-Economy-Report-
    FINAL-1.pdf
    ---------------------------------------------------------------------------
        AI is an evolving constellation of technologies that enable
    computers to simulate elements of human thinking--learning and
    reasoning among them. An encompassing term, AI entails a range of
    approaches and technologies, such as machine learning (ML) and deep
    learning, where an algorithm based on the way neurons and synapses in
    the brain change due to exposure to new inputs, allowing independent or
    assisted decision making.
        AI-driven algorithmic decision tools and predictive analytics are
    having, and will continue to have, substantial direct and indirect
    effects on Americans. Some forms of AI are already in use to improve
    American consumers' lives today; for example, AI is used to detect
    financial and identity theft and to protect the communications networks
    upon which Americans rely against cybersecurity threats.
        Moving forward, across use cases and sectors, AI has incredible
    potential to improve American consumers' lives through faster and
    better-informed decision making enabled by cutting-edge distributed
    cloud computing. As an example, healthcare treatments and patient
    outcomes stand poised to improve disease prevention and conditions, as
    well as efficiently and effectively treat diseases through automated
    analysis of X-rays and other medical imaging. AI will also play an
    essential role in self-driving vehicles and could drastically reduce
    roadway deaths and injuries. From a governance perspective, AI
    solutions will derive greater insights from infrastructure and support
    efficient budgeting decisions.
        Today, Americans encounter AI in their lives incrementally through
    the improvements they have seen in computer-based services they use,
    typically in the form of streamlined processes, image analysis, and
    voice recognition (we urge consideration of these forms of AI as
    ``narrow'' AI). The App Association notes that this ``narrow'' AI
    already provides great societal benefit. For example, AI-driven
    software products and services revolutionized the ability of countless
    Americans with disabilities to achieve experiences in their lives far
    closer to the experiences of those without disabilities.
        Nonetheless, AI also has the potential to raise a variety of unique
    considerations for policymakers. The App Association appreciates the
    efforts to develop a policy approach to AI that will bring its benefits
    to all, balanced with necessary safeguards to protect consumers.

            1.  Harmonizing and Coordinating Approaches to AI

            A wide range of federal, local, and state laws prohibit harmful
            conduct regardless of whether the use of AI is involved. For
            example, the Federal Trade Commission (FTC) Act prohibits a
            wide range of unfair or deceptive acts or practices, and states
            also have versions of these prohibitions in their statute
            books. The use of AI does not shield companies from these
            prohibitions. However, Federal and state agencies alike must
            approach the applicability of these laws in AI contexts
            thoughtfully and with great sensitivity to the novel or
            evolving risks AI systems present. Congress and other
            policymakers must first understand how existing frameworks
            apply to activities involving AI to avoid creating sweeping new
            authorities or agencies that awkwardly or inconsistently
            overlap with current policy frameworks.

            2. Quality Assurance and Oversight

            Policy frameworks should utilize risk-based approaches to
            ensure that the use of AI aligns with any relevant recognized
            standards of safety, efficacy, and equity. Small software and
            device companies benefit from understanding the distribution of
            risk and liability in building, testing, and using AI tools.
            Policy frameworks addressing liability should ensure the
            appropriate distribution and mitigation of risk and liability.
            Specifically, those in the value chain with the ability to
            minimize risks based on their knowledge and ability to mitigate
            should have appropriate incentives to do so.

            Some recommended areas of focus include:

            Ensuring AI is safe, efficacious, and equitable.

            Encouraging AI developers to consistently utilize
                rigorous procedures and enabling them to document their
                methods and results.

            Encouraging those developing, offering, or testing AI
                systems intended for consumer use to provide truthful and
                easy-to-understand representations regarding intended use
                and risks that would be reasonably understood by those
                intended, as well as expected, to use the AI solution.

            3. Thoughtful Design

            Policy frameworks should encourage design of AI systems that
            are informed by real-world workflows, human-centered design and
            usability principles, and end-user needs. AI systems should
            facilitate a transition to changes in the delivery of goods and
            services that benefit consumers and businesses. The design,
            development, and success of AI should leverage collaboration
            and dialogue among users, AI technology developers, and other
            stakeholders to have all perspectives reflected in AI
            solutions.

            4. Access and Affordability

            Policy frameworks should enable products and services that
            involve AI systems to be accessible and affordable. Significant
            resources may be required to scale systems. Policymakers should
            also ensure that developers can build accessibility features
            into their AI-driven offerings and avoid policies that limit
            their accessibility options.

            5. Bias

            The bias inherent in all data, as well as errors, will remain
            one of the more pressing issues with AI systems that utilize
            machine learning techniques in particular. Regulatory agencies
            should examine data provenance and bias issues present in the
            development and uses of AI solutions to ensure that bias in
            datasets does not result in harm to users or consumers of
            products or services involving AI, including through unlawful
            discrimination.

            6. Research and Transparency

            Policy frameworks should support and facilitate research and
            development of AI by prioritizing and providing sufficient
            funding while also maximizing innovators' and researchers'
            ability to collect and process data from a wide range of
            sources. Research on the costs and benefits of transparency in
            AI should also be a priority and involve collaboration among
            all affected stakeholders to develop a better understanding of
            how and under which circumstances transparency mandates would
            help address risks arising from the use of AI systems.

            7. Modernized Privacy and Security Frameworks

            The many new AI-driven uses for data, including sensitive
            personal information, raise privacy questions. They also offer
            the potential for more powerful and granular privacy controls
            for consumers. Accordingly, any policy framework should address
            the topics of privacy, consent, and modern technological
            capabilities as a part of the policy development process.
            Policy frameworks must be scalable and assure that an
            individual's data is properly protected, while also allowing
            the flow of information and responsible evolution of AI. A
            balanced framework should avoid undue barriers to data
            processing and collection while imposing reasonable data
            minimization, consent, and consumer rights frameworks.

            8. Ethics

            The success of AI depends on ethical use. A policy framework
            must promote many of the existing and emerging ethical norms
            for broader adherence by AI technologists, innovators, computer
            scientists, and those who use such systems. Relevant ethical
            considerations include:

            Applying ethics to each phase of an AI system's life,
                from design to development to use.

            Maintaining consistency with international conventions
                on human rights.

            Prioritizing inclusivity such that AI solutions benefit
                consumers and are developed using data from across
                socioeconomic, age, gender, geographic origin, and other
                groupings.

            Reflect that AI tools may reveal extremely sensitive
                and private information about a user and ensure that laws
                require the protection of such information.

            9. Education

            Policy frameworks should support education for the advancement
            of AI, promote examples that demonstrate the success of AI, and
            encourage stakeholder engagements to keep frameworks responsive
            to emerging opportunities and challenges.

            Consumers should be educated as to the use of AI in the
                service(s) they are using.

            Academic education should include curriculum that will
                advance the understanding of and ability to use AI
                solutions.

            10. Intellectual Property

            The protection of intellectual property (IP) rights is critical
            to the evolution of AI. In developing approaches and frameworks
            for AI governance, policymakers should ensure that compliance
            measures and requirements do not undercut safeguards for IP or
            trade secrets.

        The Chair. Thank you, Mr. Reed, and on that last point we
    will have you expand today or more generally for the record
    what we need to do about that last point.
        I definitely think in the past we have sided too much with
    the big companies on the patent side and not enough empowerment
    of the inventors--the smaller companies.
        So I want to make sure we get that part right in addition
    to the--your recommendations. You have made a couple of very
    good recommendations. Thank you.
        I would like to go to a couple of charts here, if we could.
    One, when we first started working on the privacy law--I am
    going to direct this to you, Professor Calo--but what got me
    going was the transfer of wealth to online advertising.
        I do not think people really quite understood how much the
    television, newsprint, radio, magazine, the entire advertising
    revenue shift, went online. Now we are just talking about the
    internet.
        Could you bring that a little closer, please? Get that up a
    little closer. So we are now at--oops. We are now at 68
    percent.
        I do not know if people can see that, but we are now at 68
    percent of all spending--two-thirds of all spending, of
    advertising, has now taken place online with data and
    information.
        So that trend is just going to keep continuing. Now, you
    and I have had many conversations about the effect of that on
    the news media having community voices. Our community in
    Seattle, King Five, or the Seattle Times could not exist if it
    had misinformation. It just would not--it would not exist. But
    in the online world you can have misinformation. There is no
    corrective force for that. But all the revenue has now gone to
    the online world.
        And the second chart describes, I think, a little bit about
    your testimony that I want to ask a question about and that is
    the amount of information that is now being derived about you
    that AI is this capacity to derive sensitive insights.
        So that trend that I just described where two-thirds of all
    advertising revenue--I mean, somebody said data is like the new
    oil. It is just where everybody is going to go and make the
    money.
        So that is a lot of money already in that shift over that--
    those years that I mentioned on the chart. But now you are
    saying they are going to take that information and they are
    going to derive sensitive information about us.
        Ms. Kak said it is the way your voice sounds. You have
    described it as various features. So could you tell me how
    protecting us against that in the AI model, why that is so
    important?
        And I just want to point out we are very proud of what the
    Allen Institute is doing on AI. We think we are the leaders in
    AI applications. We are very proud of that both in health care,
    farming, energy.
        We have an agreement today between the United States and
    Canada in principle on the Columbia River Treaty. I think water
    AI will be a big issue of the future--how do you manage your
    natural resources to the most effective possible use.
        So we are all big on the AI implications in the Pacific
    Northwest but we are very worried about the capacity to derive
    sensitive insights and then, as you mentioned, an insurance
    company or somebody using that information against you.
        Could you expound on that, please?
        Mr. Calo. Absolutely. I was talking to my sister, who is on
    the board of the Breast Cancer Alliance, about my testimony and
    she said, you know, Ryan, just make sure that people know how
    important it is for AI to be able to spot patterns in medical
    records to ensure that people get better treatment, for
    example, for breast cancer, and I agree with that.
        And I am also proud of all the work we are doing at the
    University of Washington and Allen. The problem is that the
    ability to derive sensitive insights is being used in ways that
    disadvantage consumers and they are not able to figure out what
    is going on and fight back.
        So, for example----
        The Chair. Thereby driving up costs?
        Mr. Calo. For example, right. I mean, you know, we know why
    everything costs $9.99. It is because your brain thinks of it
    as being a little bit further away from $10 than it really is.
        But the future we are headed to and even the present is a
    situation where you are charged exactly as much as you are
    willing to pay in the moment. Say, I am trying to make dinner
    for my kids and I am just desperately trying to find a movie
    for them to stream that they both can agree on.
        If Amazon can figure that out or Apple can figure that out
    they can charge me more in the moment when I am flustered and
    frustrated because they can tell.
        If that sounds farfetched, Senator, Uber once experimented
    with whether or not people would be more willing to pay surge
    pricing when their batteries were really low on their phone
    because they would be desperate to catch a ride.
        Amazon itself has gotten into trouble for beginning to
    charge returning customers more because they know that they
    have you in their ecosystem. This is the world of using AI to
    extract consumer surplus and it is not a good world and it is
    one that data minimization could help address.
        The Chair. Thank you.
        Senator Wicker.

                    STATEMENT OF HON. ROGER WICKER,
                     U.S. SENATOR FROM MISSISSIPPI

        Senator Wicker. Well, thank you very much, Madam Chair.
        Mr. Reed, let me go to you. You have testified before on
    this topic. Where are most of the jobs created in the United
    States economy? What size business?
        Mr. Reed. So small businesses are the single largest source
    of new business--of new jobs in the United States.
        Senator Wicker. OK. And so let us talk about how this--the
    current situation affects small and medium sized businesses and
    how legislation would affect them positively or negatively.
        Let us pretend I am a small business owner in the state of
    Washington or the state of Mississippi and I use the Internet
    to sell products in multiple states. That would be a very
    typical situation, would it not?
        Mr. Reed. Yes. In fact, one of the hardest things that I
    think has been transformative but yet it is hard for
    legislatures to understand is our smallest members are global
    actors.
        My smallest developers are selling products around the
    world and are developing a customer base around the world, and
    in many ways you can think of it this way. Maybe there is 5,000
    people in your hometown who want to buy your product but there
    is 500,000 people who want to buy your product everywhere.
        So as a small business the Internet allows you to reach all
    of those customers. The problem with APRA carving out small
    business is all of the sudden now a small business that wants
    to grow from 10,000 customers in the great state of Mississippi
    to 500,000 customers throughout the United States. Has to
    comply.
        Senator Wicker. So we--the law needs to apply evenly----
        Mr. Reed. Correct.
        Senator Wicker.--to all actors. Let us talk about
    preemption.
        If I am this small businessperson in the state of
    Washington and Congress passes a new data privacy law but the
    preemption clause has enumerated exceptions for various
    reasons. How is that going to affect me?
        Mr. Reed. Once again, the people who are best equipped to
    deal with complex compliance regimes are very large companies
    that hire large numbers of lawyers.
        So we really need a compliance regime and a preemption
    regime that is easy to understand and is applicable. When my
    businesses do not have a compliance team--heck, they do not
    even have a compliance officer. It is probably the chief
    dishwasher is also the chief compliance officer. And I think
    that is an important thing to understand about small businesses
    is they do not have teams of lawyers.
        Senator Wicker. How about the private--a broad private
    right of action? How is that going to affect me and my
    business?
        Mr. Reed. Well, I think it is interesting that you bring it
    up. When I testified before you on the same topic we had great
    discussions about the fact that there may be needs occasionally
    for private right of action but they should be limited in scope
    and I think that is more true now than ever before.
        If we have a privacy regime that exposes small business and
    everyone else to individual private right of action from
    multiple states it sets up small businesses to be the best
    victim for sue and settle.
        Nobody wants to go to war with Microsoft but I can send
    you, a small business, a pay me now note for $50K. I am going
    to go to my local small town lawyer, I am going to say, can you
    fight this?
        He is going to say, it is going to be $150,000 to fight it.
    So you are going to stroke a check and you are going to not pay
    an employee or not hire someone for $50K.
        So we want to avoid a private right of action that leads to
    the ability for unscrupulous lawyers to make a killing off of
    sue and settle, particularly in small businesses where the cost
    of fighting is greater than the cost of the check they are
    asking for.
        Senator Wicker. OK. We are going to run out of time real
    quick.
        But, Mr. Calo, on page 6 of your testimony you say
    expecting tech companies to comply with a patchwork of laws--
    and that would be small and large tech companies--a patchwork
    of laws depending on what state a consumer happens to access
    their services is unrealistic and wasteful.
        I hear people say this, let us pass a nationwide data
    privacy act. But if a state like California wants to really,
    really protect even better let us let them do so. Let us give
    states like that an option out ability. Does that not start the
    patchwork all over again?
        Mr. Calo. Senator, it is an excellent question.
        My view is that in a perfect world the Federal Government
    would set a floor and then the individual states, if they
    wanted to be more protective of their own residents, would be
    able to raise that that floor for their own citizens.
        In this particular context it is very difficult to deploy
    these large global systems in ways that differentiate depending
    on what state you are in.
        Senator Wicker. OK. And I am sorry I have to go.
        Mr. Reed, that is getting back to the patchwork, is it not?
    So now you got to comply with two and then who is going to
    decide which is more protective?
        Mr. Reed. Absolutely. That is it in a nutshell. Try and
    figure out what those are.
        The Chair. I think Mr. Calo--I think Mr. Calo just said it
    is not realistic to have the patchwork. I think his testimony
    is quite clear. He says you have to have a Federal preemption.
        Mr. Reed. [Off mic.]
        The Chair. Yes, it--I do. I think it does and I think, I
    think, Mr. Reed, I think your----
        Senator Wicker. OK. Well, Madam Chair, since there are not
    dozens of us but only a couple----
        The Chair. Senator, we have one of our colleagues who is
    waiting, Senator Rosen, who actually has coded before so I feel
    like we need to defer to her.
        So Senator Rosen.

                    STATEMENT OF HON. JACKY ROSEN,
                        U.S. SENATOR FROM NEVADA

        Senator Rosen. Well, thank you. Writing that computer code
    it was a great experience, a great living, and I loved every
    minute of it and now it prepares me for a lot of things I do
    here today.
        So thank you for holding this hearing. These issues are so
    important. I really appreciate the witnesses.
        I am going to talk first a little bit about AI scams
    because Americans are generating more data online than ever
    before, and we know with advances in AI, data can be used in
    many ways depending on the algorithms that are written. And, of
    course, bad actors are going to use AI to generate more
    believable scams using the deep fake, cloning, and all the
    pictures. You have seen it. Everyone has seen it everywhere,
    and these particularly target our seniors and they target our
    veterans.
        And so I would like to ask each--Ms. Kak and Professor
    Calo, both of you, how can enacting a Federal data privacy law
    better protect individuals from these AI-enabled cyber attacks
    and scams that we know are happening every single day in every
    community?
        Ms. Kak. Thank you, Senator.
        Senator Rosen. Ms. Kak, we will start with you and then we
    will go to the professor.
        Ms. Kak. Thank you, Senator.
        It is interesting because when ChatGPT was released and
    there was all of this excitement about whether we were one
    step--who knows if it is tomorrow or in 50 years but one step
    away from these sort of fantastical Terminator like scenarios.
        What we were really more concerned about was had we just
    seen the creation of the most effective spam generator that
    history had ever seen.
        And so I think what is at the heart of these issues is that
    we are creating these technologies that are moving to market
    clearly before they are ready for commercial release and they
    are sort of unleashing these diffuse harms including, as you
    mentioned, the risk of sort of exacerbating concerns of
    deceptive and spam content.
        To your question on how would a Federal data privacy law
    sort of nip these kinds of problems in the bud, I think it
    would do so in a very sort of structural sense.
        It would say that there need to be certain rules of the
    road. There need to be limits on what companies are able to
    collect, the ways in which they are training their models, so
    that these companies are not creating inaccurate sort of
    misleading AI tools which are then being integrated into our
    most sort of sensitive social domains, whether that is banking
    or health care or hiring.
        So I think the--sort of a final thing I will say on the
    kind of spam generation point is that we have known for the
    last decade--we have evidence that garbage in, garbage out.
        So when we see these failures we should see them not as
    output failures but as failures that go back to very crucial
    data decisions that are made right from the training and
    development stage of these models all the way through to the
    output stage and so the privacy risk is----
        Senator Rosen. Thank you. Thank you. I have--Professor
    Calo, if you could be really brief because I want to get to a
    question about data ownership--who owns your data and who has a
    right to your data. So if you could be really brief so I could
    get that question in I would surely appreciate it.
        Mr. Calo. I think that we need to empower Federal
    regulators such as the Federal Trade Commission to go after all
    kinds of abuses that involve artificial intelligence including
    such scams. Investing in the FTC and its expertise is the
    correct call, in my opinion.
        Senator Rosen. Thank you. I appreciate that because I want
    to talk about something that everyone talks to me about is data
    ownership, AI transparency, because Nevadans today, people
    across this country, do not have the right to access, correct,
    or delete their data.
        Who owns your data? What do they do with it? How do they
    use it? It matters to people, and so it is impossible for many
    to even understand, like I said, who holds their data and what
    they are going to do with it.
        So the supply chain of consumer data it is full of
    loopholes whereas third party resellers they can just sell your
    data to the highest bidder.
        So, Mr. Tiwari, can transparent AI systems exist without
    strong Federal privacy regulations including consumer control
    over your own personal data?
        Mr. Tiwari. Thank you, Senator, and in short the answer is
    no.
        It is impossible for users to be able to effectively
    exercise the rights that they have not only over their own data
    but also their social experiences without knowing what
    companies are collecting, how that data is being used, and,
    more importantly, what rights do they have if that harm is
    occurring in the real world.
        We have already in this hearing so far discussed various
    examples of harms that we have seen occur in the real world
    but, yet, the actions that regulators and governments have been
    able to take to limit some of those harms have been constrained
    by the lack of effective and comprehensive Federal privacy
    legislation.
        Senator Rosen. Thank you. I appreciate it.
        And Madam Chair, I am going to yield back with 8 seconds to
    go.
        The Chair. Thank you.
        Senator Blackburn.

                  STATEMENT OF HON. MARSHA BLACKBURN,
                      U.S. SENATOR FROM TENNESSEE

        Senator Blackburn. Thank you so much, Madam Chairman. I am
    so delighted we are doing this hearing today, and as we have
    talked so many times when I was in the House and our Senate
    colleague Peter Welch was in the House, we took steps and
    introduced the first legislation to make businesses take steps
    to protect the security of our data, to require data breach
    notifications, and allow the FTC and the state attorneys
    general to hold companies accountable for violations.
        And as Senator Rosen just said, it is so vitally important
    to know who owns the virtual you--who has that and what are
    they doing with it and now as we are looking at AI I think
    Federal privacy legislation is more important than ever because
    you have got to put that foundational legislation in place in
    order to be able to legislate to a privacy standard and that is
    why we are working so carefully on two pieces of legislation--
    the No Fakes Act that Senator Coons, Klobuchar, Tillis, and I
    are working on to protect the voice and visual likeness of
    individuals from unauthorized use by generative AI and then,
    Madam Chairman, you have mentioned the COPIED Act that you and
    I are working on, which would require consent to use material
    with content provenance to train AI systems.
        And I want to come to each of you and let us just go down
    the dias as we are looking at this. We are talking about ways
    that I would like to hear from you all very quickly--ways that
    Congress is going to be limited in legislating if we do not
    have a privacy standard, and then how do we find the balance
    between that privacy and data security component so that people
    know they have the firewalls to protect their information and
    keep it from being used by open source and large language
    models?
        So let us just run down the dais on this.
        Mr. Calo. Thank you, Senator.
        I was really struck in my research for this hearing by
    Pew--the Pew Center's research. Their survey suggests that
    overwhelming percentages of Americans are worried that their
    data is out there and it is going to be used in ways that is
    concerning and surprising to them.
        I think without passing laws that per Senator Cantwell's
    remarks define sensitive information to cover not merely
    already sensitive information like health status but cover the
    inferences that can be made on top of that data Americans are
    not going to feel comfortable and safe.
        I think that security and privacy go hand in hand. We could
    sit here and talk about the myriad horrible data breaches that
    have been occurring across our country. We can talk about
    ransomware and the way that hospital systems are being shut
    down.
        But ultimately it all boils down to the fact that the
    American consumer is vulnerable and it needs its government to
    step in and set some clear rules.
        Senator Blackburn. Thank you.
        Ms. Kak. Thank you, Senator.
        I will say that the sort of incentives for irresponsible
    data surveillance have existed for the last decade. What AI
    does is it pours gasoline on these incentives.
        So, if anything, we have a situation where all of our known
    privacy and security harms and risks are getting exacerbated.
        To the second part of your question, which is what is the
    connection between privacy and security, I would say those are
    sort of two sides of the same coin.
        So data never collected is data that is never at risk, and
    data that is deleted after it is no longer needed is also data
    that is no longer at risk.
        So I think having a strong data minimization mandate that
    puts what we would consider a very basic data hygiene in place
    is absolutely essential, especially as you are seeing more kind
    of concerning bad actors use this information in nefarious
    ways, some of which the legislation you mentioned is going to
    clamp down on.
        Senator Blackburn. Thank you.
        Mr. Tiwari. Thank you, Senator.
        Mozilla is an organization that has hundreds of millions of
    people that use its products because of its privacy properties.
    Without providing a consistent standard that allows American
    companies to compete globally just like they do on innovation
    but also on privacy, the Congress will be unable to ensure that
    Americans not only get the privacy rights they deserve but also
    that American companies can have a high baseline standard with
    which they can compete with organizations around the world.
        Thank you.
        Senator Blackburn. Thank you.
        Mr. Reed. And I understand I am over time here, but very
    quickly, privacy laws should have a data security provision. It
    is one of our four Ps of privacy.
        I think data hygiene is absolutely critical but it is
    different than a prohibition on processing so let us be careful
    on how we use the term data hygiene rather than no collection
    at all.
        So let us be smart about how we do that. Thank you.
        Senator Blackburn. Thank you, Madam Chair.
        The Chair. Thank you, and thank you for your leadership on
    the COPIED Act. I think your understanding of creators,
    artists, and musicians and being able to stick up for them has
    been really critical. So thank you.
        Senator Hickenlooper.

                 STATEMENT OF HON. JOHN HICKENLOOPER,
                       U.S. SENATOR FROM COLORADO

        Senator Hickenlooper. Thank you, Madam Chair.
        State privacy laws and Federal proposals agree that
    consumers should have more control, should have control over
    their personal data including the right to have their data
    deleted, as has been pointed out.
        However, consumers really do not have the time or the
    expertise to go down and effectively manage all their data
    online, to fill out the forms to--you know, cookie notices or
    these lengthy privacy agreements become more of a distraction.
        So, Mr. Calo, let us start with you. The American Privacy
    Rights Act proposes, A, as has been discussed, minimizing
    personal data collection, but in addition offering consumer-
    facing controls like data deletion requests and how do these
    two approaches work in tandem to protect consumers rather than
    anyone alone?
        Mr. Calo. That is a great question. In other contexts where
    we are trying to protect consumers we do give them information
    and choices, and we know that privacy preferences are not
    completely homogeneous.
        However, in other contexts not only do we give people
    choice--for example, how much fat content do they want in their
    milk--we also place substantive limits like there cannot be so
    much arsenic.
        And so I think that needs to be a combination of both.
    People should have control over their data and they should be
    asked before that data is used in a separate context like to
    set their insurance premiums.
        But there also have to be baseline rules because, as you
    point out, consumers do not have the time or the wherewithal to
    police the market and protect themselves on their own.
        Senator Hickenlooper. All right. Good answer.
        And, Ms. Kak, you can opine on that as well but I have got
    another question so let me start with the question.
        And you guys have discussed a little bit the advances in
    generative and traditional AI and how those advances really are
    fueled by data now.
        I mean, we recognize that. But training AI systems cannot
    be--it really cannot be at the expense of people's privacy and
    reducing the amount of personal sets of data, as you have all
    discussed, the notion of minimization does really reduce the
    likelihood that data privacy and data security harm could
    happen.
        And Senator Blackburn, among all the other bills she listed
    these issues are covered extensively in various hearings we
    have had on our subcommittee that she and I chair, Consumer
    Protection, Product Safety, and Data Security.
        So, Ms. Kak, I want to say how would you quantify--how
    often or what types of quantification do you say when you--when
    you say how often is the data of consumers unnecessarily
    exposed within the AI model and do you believe that the strict
    data minimization requirements can significantly help control
    this--let us call it data leakage?
        Ms. Kak. Senator, there are two parts--two ways in which I
    will answer that question. The first is to say we do not know
    and that is sort of part of why we are here today, which is
    that we do not have basic transparency about whether our data
    is being used, how it is being protected.
        And what we do know is that companies like Meta and Google
    are sort of at-will changing their terms of service to say,
    heads up, we are now using your data for AI. At the same time
    as we are seeing these chatbots routinely leak the personal
    information they were trained on.
        But I think if we did--in the absence of clear data from
    these companies and a regulatory mandate that allows us to ask
    them for it I think what we can already see just from the most
    obvious lapses is that this irresponsible data collection and
    use is happening everywhere. It is happening all the time, and
    I think one of the ways in which the U.S. might benefit from
    being somewhat of a laggard when it comes to data privacy law
    is to look at what has not worked elsewhere.
        What has not worked is a consent-only based regime. That is
    why we need accountability in addition to consent. What has
    worked is that the Brazilian data protection regulator recently
    banned Meta from using user data to train their AI because they
    found that there was children's images in the training data and
    it was being leaked on the other side.
        So there is a lot to learn and there is a lot of, I think,
    a foundation from which to act from.
        Senator Hickenlooper. Great.
        Mr. Tiwari, just quickly--the general data protection
    regulation--the GDPR--of the European Union has been in effect
    since 2018, I guess--2019, 2018. Since then it has been
    amended. They are still sorting it out, I think, in terms of
    governance structure.
        Without a U.S. data privacy law how can we resume--have
    some leadership on the global stage on these issues?
        Mr. Tiwari. Thank you, Senator.
        By most counts there are currently at least 140 countries
    around the world that have a national Federal privacy law. By
    not having a privacy law the United States of America is not
    allowing its companies to be able to effectively compete with
    the companies from these countries and that is because privacy
    is now a competitive differentiator.
        Like I mentioned earlier, people use the Firefox product
    because they believe in the privacy properties of the Firefox
    product, and without a baseline of such standards the small and
    medium companies that we have been talking about will be unable
    to compete with other small and medium companies around the
    world.
        Senator Hickenlooper. Great. Thank you very much. I yield
    back to the Chair.
        The Chair. Thank you.
        Senator Moran.

                    STATEMENT OF HON. JERRY MORAN,
                        U.S. SENATOR FROM KANSAS

        Senator Moran. Chairwoman, thank you very much, and thanks
    to our panelists. Very important hearing. Thank you for holding
    it.
        Passage of Federal data privacy legislation is long
    overdue. I chaired the Subcommittee on Data Privacy that
    Senator Hickenlooper now chairs and Senator Blackburn is the
    Ranking Member. Senator Blumenthal was the Ranking Member.
        We have come so close so many times but never just quite
    across the finish line and the problems and challenges with our
    lack of success continue to mount and it gets--I do not know
    that the issues get more difficult to resolve but we still have
    not found the will to overcome kind of the differences of the
    things that each of us think is pretty important.
        I reintroduced my Comprehensive Data Privacy--Consumer Data
    Privacy and Security Act again in this Congress. It gives
    Americans control over their own data, establishes a single,
    clear Federal standard for data privacy, and provides for
    robust enforcement of data privacy protections that does not
    lead to frivolous legal actions that would harm small business.
        I think these are common sense policies and, again, I think
    there is a path forward utilizing those and I would, again, say
    in this hearing as I have said numerous times over the years I
    am willing to work with any and all to try to find that path
    forward.
        I have a few questions for the panel. I think I will start
    with you, Mr. Reed.
        Important that data privacy requirements, in my view,
    established by Federal law are shared by consumer-facing
    entities, service providers, and third parties, all of which
    may collect or process consumer data.
        Exempting segments of this value chain from requirements or
    enforcement under the data privacy law I think places an unfair
    burden on consumer-facing entities including particularly small
    business.
        Mr. Reed, is that something you agree with when it comes to
    data privacy? Regulatory burdens should be shared across the--
    each entity that collects or processes data?
        Mr. Reed. I do, but I want to be careful about one thing. I
    do not want to--I know this sounds weird coming from the
    business side, so to speak, but I want to be careful that we do
    not say that shared responsibility becomes everybody touching
    their nose and saying, I am not it.
        So I think the point at which you give your data over, the
    point at which you have that first contact, whether it is my
    members through an application or through a store that you
    visit is the most logical point for the consumer to begin to
    say, hey, I want my data to be retrieved or I do not want my
    data used in this way.
        So I think, yes, there is shared responsibility across the
    chain but I do not want a situation where the front person who
    interacts with the customer can then say, hey, that is down the
    food chain three third parties from there. So I think it is
    important.
        Senator Moran. And you avoid that--you avoid that by?
        Mr. Reed. Well, you avoid that by actually having a clear
    and concrete conversation, so to speak, with the customer when
    they provide the data--here is what you are getting in
    exchange, here is what the rules of the road are--and that is
    why a Federal comprehensive privacy bill--and we appreciate the
    bill that you have worked on already on data--moves us in the
    direction of having consumers nationally have an understanding
    of what their rights and what our responsibilities are with
    their data.
        So absolutely, but it has to start with an understanding at
    the first place and then the shared responsibility throughout
    the chain.
        Senator Moran. One more for you, Mr. Reed.
        Nineteen states, I think, is the number that have passed
    their own data privacy laws. It is a patchwork of state data
    privacy laws, increasing compliance costs.
        One estimate projects compliance costs for business could
    reach $239 billion annually if Congress does not implement a
    data privacy law. Incidentally, I came to Congress believing
    and still believe that government closest to home is better
    than government far away.
        But it seems like I spend a bunch of my time trying to
    figure out how we take care of a complex situation with 50
    different states and 50 different standards. Kansas does not
    have a data privacy law but borders two states that do.
        Mr. Reed. Exactly.
        Senator Moran. Would you describe the challenges for small
    business associated with working in states with different data
    privacy standards?
        Mr. Reed. Well, your state is one of the ones in particular
    that we find most interesting because you literally have
    businesses in your state that have a primary number of their
    customers are actually from the bordering state because you
    have that crossroads that exist.
        So for Kansas and Oklahoma you have customers across the
    border literally every day so having a mixture of these privacy
    bills. Now, to be clear, a lot of states have some sort of
    small business carve out but it is different and the
    definitions are different in every state.
        So any business that participates is going to have to
    figure out who is going to tell them what they do and if that
    customer walks in and says, my zip code is this, oh, sorry, I
    have to treat your data this way. If it is this then I have to
    do it another way.
        It is soul crushing for a small business to have to
    interview each purchaser and ask them what county they live in
    or what state they live in. So it is a huge burden on small
    business and, unfortunately, it is one that is really a lot
    easier for a multinational company to handle.
        Senator Moran. Let me quickly ask Mr. Tiwari and Ms. Kak.
    it is important for Americans to understand when their data is
    collected and processed by companies. This belief is reflected
    in our data privacy legislation which requires covered entities
    to publish their privacy policies in easy to understand
    language and to provide easy to use means to exercise their
    right to control over data.
        How can a Federal policy ensure consumers are aware their
    data is being used even as AI potentially increases the
    complexity of how consumer data is processed?
        Mr. Tiwari. Thank you, Senator.
        It is essential for us to recognize that purely relying on
    consent has proven to be ineffective to protect the privacy of
    the average consumer.
        Technology has now become such a complex endeavor that to
    expect an average consumer to understand everything that can
    happen when their data is collected is a burden that they will
    never be able to meaningfully fulfill and, therefore, any
    effective Federal privacy legislation must include
    accountability measures that also place obligations upon these
    entities for what they cannot do regardless of whether the
    consumer has consented to that behavior or not.
        Only then can consumers be sure that the government is
    working effectively to predict--to protect their privacy rather
    than hoping that they understand everything that may happen to
    that data.
        Senator Moran. It is a sad state of affairs, actually. We
    cannot understand it well enough to protect ourselves.
        Anything to add?
        Ms. Kak. Yes. The only thing I would add, Senator, is sort
    of what do we do as consumers with that information and that is
    really why transparency is not enough and why proposals like
    the bipartisan ADPPA and the APRA are so important because they
    are putting down sort of rules of the rule that apply
    regardless of consent and what consumers choose.
        Senator Moran. Thank you both.
        The Chair. Yes. I think Senator--Senator Budd.

                      STATEMENT OF HON. TED BUDD,
                    U.S. SENATOR FROM NORTH CAROLINA

        Senator Budd. Thank you, Chair.
        So thank you all for being here. Technological leadership
    is foundational to American dominance in the 21st century.
        In this country we innovate, create products and services
    that consumers want, which increase productivity and it
    sharpens competition. This spurs a positive cycle of further
    innovation. The Internet is a perfect example of that.
        And one of the factors that differentiates America is it is
    a regulatory environment that protects public interest and
    safety while at the same time giving talented entrepreneurs and
    specialists the space to try new things.
        I think that AI should follow this tried and true path.
        Mr. Reed--thanks again to all of you for being here. Mr.
    Reed, thank you for your opening example a few moments back in
    your opening comments about hog farms. Being from North
    Carolina I really appreciate that and makes me only wish I had
    a little more bacon for breakfast. So----
        Mr. Reed. That was a good call.
        Senator Budd. That is right. Well, in your testimony you
    talked about the different ways that App Association members
    are creating and deploying AI tools. In fact, you said that 75
    percent of surveyed members report using generative AI.
        Do you believe that there is currently a healthy amount of
    competition in this space?
        Mr. Reed. Well, I think what has been most amazing is the
    level of competition against bigger players is profound. The
    news is always covering Microsoft's moves and Meta's moves and
    other players' moves but I am telling you right now that we are
    seeing more moves by small and medium sized companies to use AI
    in important ways.
        And one quick and critical example--if you have got a small
    construction business in a town, right now to bid on an RFP
    that gets let it is a lot of nights of coffee and desperate
    typing on your computer to try to get one RFP out.
        But if I can look at all of your RFPs with AI, look at what
    you do best, what you claim you do best, and help you write
    that RFP so that instead of filing one you file 10. Maybe you
    win two bids. Now you hire three people. I used your own data.
        A lot of my panelists are talking about using consumer data
    and sucking in consumer data. What my members are doing with a
    lot of this is actually using your private data stores to look
    at what you are doing well and help you improve upon it, and I
    think when we talk about AI and privacy we have to remember
    that the ability to do something like that, simply help you
    write 10 RFPs, is a huge advantage that AI provides and it is
    something, frankly, small businesses are doing better than the
    large businesses right now.
        Senator Budd. I appreciate that example, especially the
    advantages for small businesses. I read about four newsletters
    a day on this very topic so I am fascinated with it.
        The question, Mr. Reed, is about the FTC's antitrust policy
    that seems to be focused against vertical integration and we
    think it may have a chilling effect on your members' ability to
    develop and roll out new and better AI services.
        Even with some of the bigs we see--like, with Open AI, of
    course, people read news about that every day but Microsoft and
    Apple not even having a board presence there.
        I do not know if that is fear against antitrust legislation
    or what but we see that there is potentially a chilling effect.
    Do you having any thoughts on that?
        Mr. Reed. Yes. Unfortunately, the Federal Trade
    Commission's proposed rule on Hart-Scott-Rodino is terrible.
        I probably should use better words here in the Committee
    but it really puts small businesses at a huge disadvantage
    because it essentially establishes a floor for the potential
    for an acquisition, and what that does for small AI companies
    that are trying to figure out how to make their way in the
    world is to seek venture capital venture capitals--venture
    capital or even your parents they have to believe in your dream
    and part of that dream is that you can have a huge success.
        When venture capitalists are putting money in they are
    looking at a portfolio and they know nine out of 10 are going
    to fail. But if the FTC is essentially saying that they are
    capping at $119 million anybody's success level then that tells
    the venture capitalists to change the nine companies they are
    investing in because they cannot dream bigger than $119
    million.
        We also think that the Hart-Scott-Rodino proposal as put
    forth violates the Reg Flex Act because they actually did not
    take into consideration the impact on small and medium sized
    businesses.
        So yes, it has a huge impact on competition for small new
    AI startups and we are incredibly disappointed and we look
    forward to seeing if we can convince the FTC to do the right
    thing and change their way.
        Senator Budd. I appreciate your thoughts on that.
        Back to your comments on small business. I do not know if
    that weighs into your answer on the next question, Mr. Reed,
    but how should this committee weigh the need for firms to be
    able to use responsibly collected consumer data with the
    serious concerns that consumers have about the fact that their
    sensitive data may be breached or improperly used? How do we
    look at that as a committee?
        Mr. Reed. Well, as a committee the first thing to do, which
    you have heard from everyone about two dozen times, is pass
    comprehensive privacy reform in a bipartisan way because that
    gives businesses the rules of the road on how to behave with
    consumer data.
        And figuring out how we balance data hygiene, data
    minimization, and all those questions are going to be part of
    the hard work that the Senate has to do on this topic.
        But, overall, I would anchor it in the concept of harms--
    what harm is being done, what is the demonstrable harm, and how
    do you use the existing law enforcement mechanisms to go after
    those specific harms.
        I think it is a lot easier to empower existing law
    enforcement action than it is to try to create a new one out of
    whole cloth and hope it works.
        Senator Budd. I appreciate your thoughts. I thank the panel
    for being here.
        Chairwoman, I yield back.
        The Chair. Thank you so much.
        Senator Klobuchar.

                   STATEMENT OF HON. AMY KLOBUCHAR,
                      U.S. SENATOR FROM MINNESOTA

        Senator Klobuchar. Thank you very much. Appropriate that we
    are doing this remotely for a tech hearing.
        I wanted to, first of all, talk about the fact that AI, and
    I have said this many times, quoted David Brooks. He says he
    has a hard time writing about it because he does not know if it
    is going to take us to heaven or hell, and I think there are so
    many potential incredible benefits coming from state of the
    Mayo Clinic that we are going to see here.
        But we have to put some guardrails in place if we are going
    to realize those benefits and not have them overwhelmed by
    potential harm, and I know a lot of the companies involved in
    this agree.
        We do have to remember when it comes to privacy these are
    no longer just little companies in a garage that resisted any
    rules for too long and whether it is competition policy,
    children's privacy, that we need to put some guardrails in
    place and it will be better for everyone.
        So I want to start--Professor Calo, your testimony
    discussed how companies can collect or buy vast amounts of a
    person's nonsensitive information from what is in their
    shopping cart to their posts on social media, and use AI to
    process all that data and make sophisticated inferences about
    their private health information such as pregnancy status,
    whatever, with alarming accuracy.
        Can you speak to how data minimization can ease the burden
    on consumers trying to protect their privacy?
        Mr. Calo. Thank you, Senator.
        Yes, it is a critical issue. So many privacy laws--almost
    all of them--differentiate between sensitive categories of
    information such as health status and more less sensitive
    information, even public information.
        But the problem with these AI systems is that they are
    extremely good at recognizing patterns in large data sets and
    so they can infer sensitive things from.
        And so how would data minimization help? Well, data
    minimization would, of course, restrict the overall amount of
    information in the categories for which it could be used.
        I think the most effective tool is to define categories of
    sensitive information to include not just sensitive information
    itself that is collected from the consumer as sensitive or
    observed somewhere but also those sensitive inferences that are
    derived from AI.
        I think that is the clearest way. That way you know as a
    corporation--please.
        Senator Klobuchar. OK. Very good. Good answer. Thanks.
        Mr. Tiwari, any comments on that, we have demand for data
    at an all-time high and will comprehensive privacy legislation,
    perhaps, sort of shape what developers and deployers do to
    adopt more privacy-preserving systems as they develop things
    which they are already doing?
        Just quickly.
        Mr. Tiwari. Thank you, Senator.
        We very quickly and sort of very recently acquired an
    organization called Anonym and what Anonym does is it takes
    privacy-preserving technologies and within trusted execution
    environments performs the operations that would take place in
    large distributed systems in a way that nobody can see the
    data, not the entity that is giving the data, not Anonym and
    nor the entity that is using the data in order to carry out, in
    this case, attribution for advertising.
        And we believe that these technologies are showing a path
    forward that not just minimizes data collection but also
    showcases that even when data is being processed similar to on
    device processing what are the ways in which it can be done
    that minimizes the risk to the average consumer and improves
    risk and liability for companies. Thank you.
        Senator Klobuchar. Thank you.
        Ms. Kak, Senator Thune and I have a bill--I think you may
    be familiar with it--the AI Research, Innovation, and
    Accountability Act--to increase transparency and accountability
    for the riskiest nondefense applications of AI.
        It directs the Commerce Department to set minimum testing
    and evaluation standards for AI systems that pose the highest
    risk such as systems used to manage our electric grid, other
    critical infrastructure.
        It also requires AI deployers to submit regular risk
    assessments and transparency reports to the Commerce Department
    that among other things document the source of the AI data used
    to train employees.
        Do you agree that transparency in the datasets used to
    train commercially available models can do more to protect
    consumer privacy but also ensure more reliable systems?
        Ms. Kak. Absolutely, and, Senator Klobuchar, we are
    actually seeing some of these big tech companies argue in their
    policy briefs that it is the training stage that is irrelevant
    and we should only be focusing on the output stage.
        But that could not be further from the truth and it is very
    clearly in service of their interests alone. So I think we
    should really double down on both transparency but also testing
    and evaluation throughout the life cycle of an AI product.
        I will also say that companies should not be getting to
    grade their own homework so the actual metrics that we use for
    this evaluation should be set by regulators. It should be set
    by public bodies and not the companies themselves.
        Senator Klobuchar. OK. Very good. Thank you, all of you. I
    have a question on voice cloning scams and one on kids' privacy
    that I will submit on the record to all of you.
        So thank you very much.
        The Chair. Senator Vance.

                    STATEMENT OF HON. J. D. VANCE,
                         U.S. SENATOR FROM OHIO

        Senator Vance. Thanks, Madam Chair. Thanks to our four
    witnesses for being here.
        I would direct my questions to Mr. Reed and appreciate in
    particular your testimony. There is this concern in this
    building and across Washington that AI poses a number of safety
    concerns, and I fully admit that there are a number of issues I
    worry about as AI continues to develop.
        In particular, you can imagine a scenario where AI makes
    these chatbots much more efficient, much more believable,
    allows predators to prey on children more easily online. That
    is a real concern and something that I think that we in this
    committee should be very concerned about, and I know that is a
    bipartisan worry.
        On the flip side of it I also worry that that legitimate
    concern is justifying some over regulation or some preemptive
    over regulation attempts that would, frankly, entrench the tech
    incumbents that we already have and make it actually harder for
    new entrants to create the innovation that is going to sort of
    power the next generation of American growth and American job
    creation.
        And I want to just get your reaction to that thought, Mr.
    Reed, and what you are seeing in your work, and the one
    additional observation I will make is very often CEOs,
    especially of larger technology companies that I think already
    have advantaged positions in AI, will come and talk about the
    terrible safety dangers of this new technology and how Congress
    needs to jump up and regulate as quickly as possible, and I
    cannot help but worry that if we do something under duress from
    the current incumbents it is going to be to the advantage of
    those incumbents and not to the advantage of the American
    consumer.
        Mr. Reed. Well, thank you for the question and, of course,
    that is the normal behavior of any company that has significant
    compliance resources is to look for ways to make sure that they
    are prepared to comply.
        Obviously, they face billion and trillion dollar risks so
    for them it is viewed through that lens of risk versus
    opportunity. So completely agree, and that is what is, to a
    certain degree, trying to shape this environment.
        What I have noticed is that AI outside of the context of
    the large headline news is really empowering the way that small
    businesses can do activities that they are currently not very
    good at and do them better.
        For example, if you are a small business owner one of the
    things that you run into all the time is am I buying my--am I
    buying my inventory at the right time? Am I on 30-day net, 60-
    day net, or 90-day net?
        Every small business owner in your state knows those terms.
    What I would like to do is I want to use AI to look at my last
    3 years of bookkeeping and figure out am I buying the wrong
    thing? Am I buying at the wrong time? Am I missing my 30-day
    net and not paying and losing money because that costs me my
    next customer and that, more importantly, costs me the next
    person I am going to hire.
        So those are the kinds of areas that we need it. I heard
    one of the other witnesses talk about we need no inferences on
    sensitive data. I am a little concerned about that as well in
    the context of what you ask.
        I have had the privilege of serving on a Federal advisory
    committee for HHS for both this and the previous administration
    and one of the things we talk about a lot is something called
    social determinants of health, and in the state of Ohio it is
    really important to figure out do people have access to
    nutrition, do they have access to the internet, do they have
    access to telemedicine--all the services that are in there and
    you need AI to help build inferences about the health and well
    being of those citizens of your state.
        So I think it is whether you are careful to have these
    blanket no inferences but, rather, talk about the real harms.
    So agree with you completely on what you said at the beginning,
    which are the real structural and functional harms that can
    happen from AI.
        But I think we should look through the lens of harms to
    make the choices that Congress has to make.
        Senator Vance. So with my brief time just one question on
    who has done data privacy in--one of the benefits of our
    Federal system, to back up a little bit, is sometimes the
    states do something that is really smart and, frankly,
    sometimes they do something that is very stupid, but we can
    sort of learn from it before projecting a national regulatory
    regime without any experience.
        Who has done this well? Who has done data privacy well?
        Mr. Reed. I think the structural model that has worked best
    so far from the states is what is sometimes called the Virginia
    model but it is Virginia, Colorado, Connecticut, Delaware,
    Montana, and Oregon have kind of taken that structural model.
        If I had any suggestion I think starting with that model as
    a path forward for the Federal Government is a good idea. It
    has a lot of broad support.
        But what we cannot have is everybody with their own flavor.
    I do not want 31 flavors of a privacy bill. I want a model that
    applies to everyone everywhere.
        Senator Vance. Yes. OK. Thank you, and thanks to the rest
    of witnesses, too. I yield.
        The Chair. Thank you.
        Senator Budd.
        Who else is--I think we are waiting for Senator Welch. OK.
        Nobody else online? Nobody else? Yes, yes. No, no, I got
    it.
        Senator Schmitt. Senator Schmitt.

                    STATEMENT OF HON. ERIC SCHMITT,
                       U.S. SENATOR FROM MISSOURI

        Senator Schmitt. Thank you, Madam Chair.
        The Chair. Thank you.
        Senator Schmitt. And I want to thank the witnesses for
    being here today, too.
        In my duties as a member of both the Senate Commerce
    Committee and the Armed Services Committee I have come to
    realize the significant implications that AI has for the
    future, not only for our government but the economy and the
    citizens of this great country.
        AI as a power--has the potential to transform many types of
    commercial applications and is already playing a pivotal role
    in various aspects of our national security apparatus.
        St Louis, Missouri, where I am from, in particular is
    helping lead that charge through Scale AI and its innovative
    data annotation and curation systems as well as its advanced
    model testing and evaluation approach.
        Scale AI is partnering with important government entities
    such as the NGA in St. Louis as well as notable commercial
    applications like OpenAI to improve AI modeling.
        Axios reported this week that U.S. private sector
    investments in AI outpace every other leading nation, more than
    doubling investments of our pacing threat China. Members of
    this committee will speak to the need potentially for
    overreaching guardrails related to AI.
        While I believe targeted measures may be warranted where
    current gaps in law may exist it is critical that we are not
    over reactive. Any new laws must not hinder investments in
    innovation being made by our private sector.
        Unfortunately, the approach by the Biden administration and
    others in this body threatens investments. Monolithic
    regulatory regimes pushed by this administration pick winners
    and losers.
        AI needs innovation both large and small. It needs
    innovators both large and small. Only the largest of the
    companies can comply with sweeping regulations, which is
    precisely why I believe big tech supports these proposals.
        There is an unholy alliance right now between the current
    administration and big tech to crowd out future competition.
    Additionally, I fear that the Biden administration's efforts on
    AI are a backdoor attempt to use regulators to silence their
    political opponents.
        I recently led a letter with Senator McConnell, Senator
    Thune, and Ranking Member Cruz calling out the efforts by the
    FCC to now police the content of political advertising through
    regulation of AI leading up to the 2024 elections.
        The goals of the administration to hide in plain sight.
    They will leave no stone unturned to use big tech and
    regulations to squash out those that they disagree with.
        While I think this hearing provides an important
    opportunity for us to hear from our witnesses and gain valuable
    insight into the greater AI and privacy ecosystem, I feel it
    incumbent upon us to also ask which existing laws can address
    the fears expressed by many of our colleagues on this committee
    related to AI.
        And as you guys know, AI has dominated a lot of these
    discussions and one thing that has become increasingly clear is
    that we have many existing laws in place already that address
    conflicts involving AI.
        For example, it is currently illegal to commit mail fraud.
    It should not matter whether or not the person uses AI to write
    the letter or a pen. It is still illegal.
        We have seen other countries prematurely attempt to over
    regulate this technology and it is clear we cannot follow their
    lead.
        So my first question here to Mr. Reed, as Congress debates
    the need for regulation of AI it is clear we need to focus on
    the gaps in the system and not create a whole new regime.
        With that said, what purely new regulations do you think
    are needed based on the gaps in our existing regulatory system?
        Mr. Reed. Thank you very much for the question, and I am
    going to sound like a broken record but it is a comprehensive
    privacy bill that clearly defines what are the responsibilities
    of business on how do we communicate to you and what we are
    going to do with your data.
        Because to take a step back, almost all the questions that
    arise on this are about customers having an unexpected outcome.
    You heard earlier from the Chair. She talked about how polling
    numbers show that most Americans are convinced that something
    is going to happen with their data that they do not expect or
    is not--and not in their interest.
        That is our fault in business and that is a problem with
    the regulation because we have different ways in which we are
    supposed to communicate with customers on what we are supposed
    to do.
        So I think, step one, tell customers what we are going to
    do, step two, meet those expectations, and to your point, step
    three, have existing laws that are already on the books--go
    after us when we do not.
        And I think that is a big part of the problem.
        Senator Schmitt. Mr. Tiwari, do you agree with that?
        Mr. Tiwari. Absolutely. It is very clear that the benefits
    of having privacy legislation is something that Americans are
    already quite familiar with, thanks to laws like HIPAA for
    health, COPPA for children, that have been in the books for a
    very long time and have been remarkably effective at preventing
    some very serious harms.
        Comprehensive Federal privacy legislation will go a very
    long way in ensuring that protection is available to every
    American.
        Senator Schmitt. Mr. Reed, given the rapid pace of AI
    innovation, especially among startups and individual
    developers, can you explain how such a regulation might
    disproportionately impact, if we were to follow this
    potentially where some people want to go, disproportionately
    impacts smaller entities that lack the resources of bigger tech
    companies?
        Mr. Reed. Absolutely. I think the issue is that we have
    seen some calls for things like a Federal agency that would
    license large language models.
        It would not be surprising that the companies that will be
    able to achieve those licenses will just happen to be companies
    that state their value in trillions of dollars versus small and
    medium sized businesses.
        So what we see is that a privacy law that essentially
    cabins off the ability to meet the requirements are so high
    that no small business can ever meet it. If a company is highly
    vertically integrated and already has all of that customer data
    then they are at no risk.
        They have already gotten permission to use all that data
    and so, going forward, their answer is, fine by us because we
    have already got it.
        Senator Schmitt. Right, and I guess to follow up on that
    then, how would that concentration of power that could even be
    exacerbated than what we have right now under that kind of
    regime--how would that affect privacy, competition, consumer
    choice in the AI industry?
        Mr. Reed. Well, I think it absolutely affects consumer
    choice.
        And, to be clear, I do not want to sound too negative. We
    actually need platforms. Platforms are actually really critical
    for us to build on top of.
        So I want to see successful large language models and so I
    do not want legislation to say big companies cannot build large
    language models. That is terrible, too.
        So the right answer is what you kind of came with at the
    beginning. Look at the existing law enforcement infrastructure,
    whether it is the Office of Civil Rights at HHS, as Mr. Tiwari
    discussed, the Federal Trade Commission's ability to enforce
    COPPA.
        We already have those tools on the books. If we go the
    other direction and regulate it at this high level then it will
    be almost impossible for small companies to knock off those
    incumbent positions.
        Senator Schmitt. Well, I mean, it is sort of my final
    question then. If we go down this road then could that
    potentially then give our--an advantage to our adversaries who
    might develop or adopt a more lenient approach accelerating
    their own advancements to the detriment of what we are trying
    to do?
        Mr. Reed. Of course. The Chairwoman and everyone has
    recognized that the AI ecosystem is global in place and so our
    competitiveness is global.
        I am very proud of the fact that our members sell products
    around the world to customers everywhere. I heard mention of
    Brazil. We actually have great developers in Brazil who are
    doing amazing things, and what is amazing about that is they
    depend on the innovation that often comes from the United
    States to build the next product and the next product that
    serves a customer base that speaks Portuguese and does not
    speak English. And, yet, a lot of the innovation came from the
    platforms here that they build on top of.
        So our global competitiveness is absolutely impacted if we
    restrict the access to the technology that drives us forward.
        Senator Schmitt. Thank you. Thank you, Madam Chair.
        The Chair. Thank you.
        Senator Welch.

                    STATEMENT OF HON. PETER WELCH,
                       U.S. SENATOR FROM VERMONT

        Senator Welch. Thank you very much, Madam Chair, and I
    thank the witnesses.
        One of the big concerns that all of us have, really, is
    meaningful consumer notification and consent and how individual
    information is being used, and as I understand it large
    language models are training on massive data sets scraped from
    all across the Internet and that can include private and
    personally identifiable information.
        And concerningly to me and, I think, a lot of folks
    researchers found that ChatGPT could be tricked into divulging
    training data including user IP addresses, e-mails, and
    passwords, and software patches are not the solution that we
    need, in my view.
        So it is really the reason that Senator Lujan and I
    introduced the AI CONSENT Act and that would require online
    platforms to obtain consumers' expressed informed consent
    before using their personal data for any purpose to train AI.
        So I want to ask, Mr. Tiwari, do you believe it is more
    effective to give consumers the ability to opt in to allow
    companies to use their data or opt out once the data is already
    being used?
        Mr. Tiwari. Thank you, Senator.
        Absolutely yes. The Mozilla Foundation has run campaigns
    over the last four to five months that have explicitly focused
    on this specific question, both requiring companies to be
    transparent about the fact of whether they are using personal
    data in order to train their models and after that to ensure
    that users have complete control over this processing, meaning
    both users should be able to consent for such behavior to take
    place but also that they should be able to withdraw this
    consent whenever they like and opt out of this processing.
        We believe that the risks that exist from the leakage of
    such private information will drastically reduce if users are
    given an ability to both understand what their data is being
    used for and then to make a choice of whether they would like
    it to be used in that way.
        Senator Welch. OK. Thank you.
        Another issue here is small business. They do not have,
    obviously, the resources, the infrastructure, that big
    businesses have. Yet, consumer data can be lost or appropriated
    through that source.
        But we do not want to impose huge expenses on small
    business that they just cannot meet--the local retail florist,
    let us say. So but we want to protect people's privacy.
        So let me just ask, Mr. Calo, what would be a good way to
    deal with this? I am thinking about either a pilot program or
    the capacity of the Federal Government to provide sort of a
    punch list of things that can be done in--to assist small
    businesses so they do not have to do something that is not
    within their capacity to do. They want to sell flowers. They do
    not want to become IT experts.
        So perhaps you could tell us how we could protect people's
    privacy in a way that does not burden small business?
        Mr. Calo. What a great question. I mean, it is true that
    small businesses do not have the capacity to comply at this--
    with the same sorts of requirements at the same level as large
    businesses.
        A few years ago the Tech Policy Lab at the University of
    Washington we hosted the ``Start With Security'' series by the
    Federal Trade Commission where the FTC was saying, look, it is
    going to be unfair and deceptive. You do not have adequate
    security that is proportionate to how much data you have.
        But rather than just sort of throw that out there they went
    on a tour around all the innovation centers. They came, of
    course, to Seattle but also to San Francisco and elsewhere and
    they invited smaller companies to talk about the FTC's own
    expectations.
        The more we can do to help scaffold government expectations
    for smaller businesses the better off we will be. But I agree
    that you do need to have a tiered system because Google and
    Meta and Amazon and others have the capacity to comply with far
    more rigorous detailed rules than do small businesses.
        Senator Welch. Thank you.
        I want to ask Ms. Kak this question. Senator Bennet and I
    have introduced the Digital Commission--the Digital Platform
    Commission Act, and what it would do is establish an
    independent Federal commission to regulate digital platforms
    and that would include on key issues like AI and privacy
    concerns.
        And our theory, essentially, is that the Congress simply
    cannot keep up with everything that is happening and we cannot
    address everything in a one-off piece of legislation. There has
    to be a governmental agency that is properly staffed and
    resourced to keep up with this.
        We have done this in the past when we started the
    Securities and Exchange Commission, when we started the Federal
    Aviation Authority, when we did the Interstate Commerce
    Commission.
        Give me your thoughts on just the concept of a digital
    platform commission as having that responsibility in an ongoing
    basis.
        Ms. Kak. Thank you, Senator, for your leadership on this
    issue.
        In general we do think that independent regulation and sort
    of resourcing our enforcers is--should be a top priority. What
    I will say, though, is that I do think that existing
    enforcement agencies like the FTC have been working on these
    issues for decades now.
        They have the capacity. They have the kind of technical
    expertise. What the moment needs is for these agencies to be
    much better resourced so that they can meet the moment and for
    laws like the APRA and the bipartisan ADPPA which empower them
    to then enforce clear privacy standards.
        Senator Welch. All right. Thank you very much. I yield
    back.
        The Chair. Senator Thune.

                     STATEMENT OF HON. JOHN THUNE,
                     U.S. SENATOR FROM SOUTH DAKOTA

        Senator Thune. Thank you, Madam Chair, for holding today's
    hearing.
        It is my view that this committee, which has expansive
    jurisdiction over technology issues, should play a significant
    role in understanding how the most recent developments in AI
    will impact society and existing law and, ultimately, is my
    belief that this will include developing a thoughtful risk-
    based legislative framework that seeks to address the impacts
    of AI which is why I have introduced the AI Research,
    Innovation, and Accountability Act last year with Senators
    Klobuchar, Wicker, Hickenlooper, Capito, Lujan, Loomis--Lummis,
    I should say, and Baldwin.
        The bill establishes a framework to bolster innovation
    while bringing greater transparency, accountability, and
    security to the development and operation of the highest impact
    applications of AI.
        Through this legislation basic safety and security
    guardrails are established for the highest risk applications of
    AI without requiring a burdensome audit or stamp of approval
    from the government, and it is my hope the Committee will
    advance the legislation soon.
        Mr. Reed, there are several proposals out there calling for
    new AI oversight agency, a licensing regime that would require
    the government to approve certain AI systems before they could
    be deployed.
        The framework that I just mentioned and that we have been
    working on with members of this committee would take a more
    pragmatic approach, in my view, arguing instead that a risk-
    based compliance and assessment process would provide the
    necessary oversight while also allowing AI developers and
    research to innovate more quickly.
        In your view how would a licensing regime impact the
    innovation ecosystem in the U.S.?
        Mr. Reed. Well, as I touched on earlier, I think licensing
    regime always leads to a situation where those with the power
    and the time and the number of lawyers to go through the
    process of meeting that licensing requirement are the ones who
    win at the table rather than those that have the best
    technology or the best ideas.
        So I am always concerned about a licensing regime that is
    the only door into success. We know how that works. You end up
    with three, four, maybe five competitors and they settle into a
    nice multiyear program.
        I think beyond that I want to say that your work on the
    risk-based framework is a great idea but I am always hesitant
    because I know that we need comprehensive privacy legislation.
        It has been very hard as people come forth with these great
    ideas that offer really good alternatives but then will leave
    out a piece.
        Without comprehensive privacy legislation what ends up
    happening is we have a patchwork and that patchwork costs us.
        You made one other critical point that I think is vital,
    and I have heard it from my fellow panelists. There is, in
    fact, already existing government expertise.
        I have had the honor to work with the Federal Drug
    Administration--the FDA--and its Center for Excellence, and I
    would say that they have incredible leadership already on
    understanding AI frameworks, their development internally about
    how do I get approval for software as a medical device that
    includes AI and how do I deal with the difference between black
    box AI and how do I deal with transparency.
        The FDA has been working on this for years. So I think my
    worry about standing up a new Federal agency when we already
    have expertise on these topics within existing agencies and I
    am going to agree with Ms. Kak to say that we need to make sure
    that we have plenty of resources for them to enforce existing
    laws.
        If you have got a problem in health, OCR. If you have got
    housing we have got DOJ. You already have mechanisms. You
    already have harms-based, risk-based assessments that can be
    done by existing government agencies. It is a lot better to
    stand up--than standing up a single agency.
        And my last point on that is very simple. Anyone who has
    ever run a business knows that hiring and growing a business
    while hiring is one of the hardest things. You would be growing
    and hiring a business inside of the government called a whole
    new agency while at the same time that everything is changing.
        Let us let the experts in each area do their work rather
    than trying to build out while the plane is already in the air.
        Senator Thune. Yes. And to be clear, we do not call for any
    kind of a bureaucracy or agency.
        Mr. Reed. Exactly. Exactly.
        Senator Thune. In fact, that is the--the framework in our
    bill does not allow for that.
        Mr. Reed. We thank you for that.
        Senator Thune. That is why we want it to be a light touch
    approach.
        On the privacy issue is it your view that we are better
    served by having one national standard?
        Mr. Reed. Absolutely.
        Senator Thune. Yes. OK. And how about the Federal privacy
    law including a private right of action? What is the practical
    effect of a provision like that on small businesses and
    startups?
        Mr. Reed. The reality is that in order to achieve a
    bipartisan legislation it is likely that we are going to have
    to give some consideration to a private right of action.
        I think what has to happen is that if Congress considers
    putting a private right of action in place it needs to ensure
    that it has numerous backstops to make sure that it does not
    become an opportunity for kind of ludicrous sue and settle.
        We saw that--we have seen that where small businesses will
    get a letter saying you are using a patent in a fax machine.
    You owe me $50,000. Pay me. And the cost of fighting that
    ludicrous suit is greater than the $50,000 you send.
        I do not want our privacy laws with a private right of
    action to lead to one state being kind of the breeding ground
    for hilarious yet economically untenable action against small
    and medium sized businesses, because we are the best victims in
    this.
        We have just enough money to pay you but not enough money
    to fight you.
        Senator Thune. All right.
        Madam Chair, my time has expired. I have a question I would
    direct to Dr. Calo but I will submit it for the record having
    to do with----
        The Chair. Go ahead.
        Senator Thune. Well, let me just say that----
        The Chair. Or whatever you want to do.
        Senator Thune. Well, I just--let me just--very quickly, I
    see transparencies I mentioned as the key to ensuring that both
    developers and deployers of AI systems are accountable to the
    consumers and businesses they serve.
        But could you expound just briefly on what constitutes a
    deployer versus a developer as well as explain how obligations
    for developers and deployers differ when it comes to
    transparency and accountability?
        Mr. Calo. Yes. I mean, technology generally has a problem
    of many hands and so you have these foundational models and
    then you have APIs upon which things are built.
        And so effective legislation makes it clear what the
    respective responsibilities of each of these parties are. For
    me, personally, speaking on my own behalf, my concern enters
    into it when whoever it is in the ecosystem is leveraging the
    tool in a way that harms consumers and tries to extract from
    them.
        So maybe that is the platform but maybe it is somebody
    using the tools of the platform.
        Senator Thune. All right. Madam Chair, thank you.
        The Chair. Thank you.
        I would actually like to follow up on--and I was going to
    anyway so it was a good lead in. I want to thank Senator Thune
    for his leadership because he--not just on data security but on
    privacy and now on your proposal as it relates to AI. Very good
    concepts there that we should continue to work on and,
    hopefully, we will get to a markup soon.
        But this notion--this data that came out by the Department
    of Justice that it had dismantled a Russian bot farm intended
    to sow discord in the United States and that the AI--the
    Russians created scores of fictitious profiles and then
    generated these posts.
        And so I am just--I am trying to--you were talking about
    this ecosystem that is created and now here we have bots who
    are just exploding with information because we have given them
    so much data.
        You can say it came from the social media platform, that
    they collected it, and then that information got scraped and
    then the information got to the bots and then the bots put it
    on this accelerant and a bad actor can use it against us.
        So why is this so important to now have a tool to fight
    against this? Because the bot system is out of control but the
    AI accelerant on the bot system makes it an imperative.
        Mr. Calo. I can only agree with you, Senator.
        Obviously, misinformation and propaganda are not new but
    the ability of adversaries of all kinds, domestic and foreign,
    to create plausible looking and very damaging misinformation
    campaigns has become quite acute.
        I mean, I will just use one example. As you know, the
    Center for an Informed Public studies misinformation and
    disinformation. One example is that someone created a deep fake
    that was not real--fictitious--that gave the appearance that
    there had been a bomb go off at the Pentagon, which was so
    concerning to people that it actually caused a dip in the stock
    market until people figured out that it was not real.
        The ability to create a seemingly real catastrophic event
    is very, very dangerous but you are talking to something even
    more basic which is that AI makes it possible to generate much,
    much more disinformation and have it appear different from one
    another--different media, different phrasing, and everything
    else.
        It is deeply, deeply concerning. I think there are ways in
    which a privacy law could help actually but the problem of
    disinformation and misinformation probably is broader still.
        The Chair. So what do we do about the bots from a
    regulatory perspective?
        Mr. Calo. Yes, that is hard. I mean, so states like
    California have a bot disclosure act. It requires that if you
    are operating a bot in certain ways, commercial and
    electioneering, that you have to identify yourself as fake.
        The problem, of course, is that Russian disinformers are
    not going to comply with our laws and so I think part of the
    response has to be political and economic.
        It is one of the main reasons that the Federal Government
    needs to get involved because it is not something the states
    can address. States cannot find global consensus around
    sanctioning bad acting around information.
        But I think that placing responsibility on the platforms to
    do as much as possible since they have control over their own
    platforms to identify and disincentivize this kind of
    misinformation that is automated is also really key.
        The Chair. Thank you.
        Well, I think that concludes our hearing. I know it is a
    very busy morning for everybody. The record will remain open
    for two weeks and we ask members to submit their questions for
    the record by July 18.
        I want to thank the witnesses, all of you. A very
    informative panel. We appreciate you answering these questions
    and helping us move forward on important privacy and AI
    legislation.
        We are adjourned.
        [Whereupon, at 12:01 p.m., the hearing was adjourned.]

                                A P P E N D I X

                                     ______

       Response to Written Questions Submitted by Hon. Maria Cantwell to
                                   Ryan Calo
    Targeted Pricing
        We have seen more and more examples of how AI can exacerbate
    existing harms online. By automating processes that would be too labor
    intensive for humans to accomplish alone, AI can enable increasingly
    personalized targeting. This can be used by bad actors for fraud, but
    also by companies for advertising and even personalized pricing.
        Mozilla and Consumers International recently found that the dating
    app Tinder used dozens of variables to adjust pricing for its members,
    with U.S. users paying between $4.99 to $26.99 for the exact same
    service. Older individuals tended to receive higher prices,
    highlighting the danger of opaque pricing algorithms to discriminate.

        Question 1. Can you speak to the way these harms will increase if
    we do not put limitations on data collection and use?
        Answer. Thank you for your excellent questions, Senator, and for
    your longstanding leadership on these issues. In 1999, law professors
    Jon Hanson and Douglas Kysar coined the term ``market manipulation'' to
    refer to the ways companies profit off of the cognitive limitations of
    their consumers.\1\ One of their examples involved the use of 9s
    instead of 10s to induce so-called price-blindness: everything costs
    $9.99 because it looks farther away from $10.00 than one cent.
    ---------------------------------------------------------------------------
        \1\ Jon D. Hanson & Douglas A. Kysar, Taking Behavioralism
    Seriously: Some Evidence of Market Manipulation, 112 Harv. L. Rev. 1420
    (1999).
    ---------------------------------------------------------------------------
        Today's companies are not satisfied with such little tricks. They
    want to use what they know about consumers--plus the power of design--
    to charge each consumer as much as they are willing to pay--what
    economist call our ``reservation price.'' \2\ Examples include charging
    consumers higher prices once they become habitual users of the service,
    as Amazon did,\3\ or experimenting with whether consumers might be more
    willing to pay Uber surge pricing when their battery is low for fear of
    being stranded.\4\ This practice has become prevalent enough that the
    Federal Trade Commission recently launched an inquiry into
    ``surveillance pricing.'' \5\
    ---------------------------------------------------------------------------
        \2\ Ryan Calo, Digital Market Manipulation, 82 Geo. Wash. L. Rev.
    995 (2014).
        \3\ Jonathan L. Zittrain, The Future of the Internet--and How to
    Stop It (2008).
        \4\ Ryan Calo & Alex Rosenblat, The Taking Economy, 117 Colum. L.
    Rev. 1623 (2017).
        \5\ See https://www.ftc.gov/news-events/news/press-releases/2024/
    07/ftc-issues-orders-eight-companies-seeking-information-surveillance-
    pricing.
    ---------------------------------------------------------------------------
        Most consumers would be surprised to learn that companies are
    studying their every move in order to charge them as much as possible.
    The phenomenon will only get worse as artificial intelligence makes
    targeted pricing even more prevalent and sophisticated. Limitations on
    data collection and use would interfere with the capability of
    companies to use what they know about consumers to extract more profit.
    Deepfake Abuses with Generative AI
        A recent study by researchers with Google's DeepMind examined how
    generative AI is currently being misused and found that the most
    prevalent form of misuse is impersonating other individuals by
    manipulating human likenesses--commonly known as deepfakes.
        I've heard from my own constituents about scams and fraud using
    deepfakes, including romance scams that take a celebrity's likeness,
    manipulate it to personalize a message to an unsuspecting--often
    elderly--individual, and then request or demand money. AI can be a tool
    for bad actors to target specific individuals.

        Question 1. How can a strong privacy law help prevent these scams
    before they start?
        Answer. The artificial intelligence behind deepfakes requires an
    enormous amount of training data. Sources of this data includes what is
    available online, which incentivizes companies to scrape every corner
    of the internet, as well as the company's own internal data, which
    incentivizes them to collect as much data as possible and store it
    indefinitely. A strong privacy law could help ensure that consumer data
    is not repurposed for nefarious ends.

        Question 2. How can we ensure that bad actors using generative AI
    tools are held accountable?
        Answer. Congress can and should pass laws that penalize certain
    common, harmful uses of generative AI--such as political deepfakes and
    non-consensual pornography. But it is often hard to identify or reach
    perpetrators in time. Congress should also set expectations that
    companies providing generative AI tools to the public maintain adequate
    safeguards and respond meaningfully to complaints.
                                     ______

       Response to Written Questions Submitted by Hon. Ben Ray Lujan to
                                   Ryan Calo
        In your hearing testimony, while discussing how AI is exacerbating
    the problems of misinformation and disinformation, you stated ``I think
    there are ways in which a privacy law could help, but the problem of
    misinformation and disinformation is broader still.''

        Question 1. Can you elaborate on this statement and describe what
    concrete policy steps (both by the public sector and/or by non-public
    entities) you believe are needed to mitigate the risks of digital
    disinformation and misinformation?
        Answer. Thank you for this thoughtful question. I see two promising
    policy directions to mitigated disinformation. The first involves
    disincentivizing disinformation at the source. There are different
    reasons that individuals and groups engage in disinformation. A
    surprising volume of disinformation is motivated by a desire to reach
    gullible or vulnerable audiences and sell them questionable goods and
    services.\6\ The government should penalize this strategy. Other
    disinformation originates abroad and aims to undermine American
    democracy. Here, political and economic pressure may be more effective
    than domestic laws.
    ---------------------------------------------------------------------------
        \6\ See https://chequeado.com/investigaciones/how-disinformation-
    makes-money.
    ---------------------------------------------------------------------------
        The second strategy involves expecting more from the platforms
    where disinformation originates and spreads. Federal law shields social
    media from liability for most categories of disinformation. But
    government can still act. Requiring companies to identify, report on,
    and commit resources to mitigate disinformation on their platform is
    not the same as treating the platform as the publisher of this
    content--which is what the Telecommunications Act of 1996 actually
    forbids via Section 230.\7\
    ---------------------------------------------------------------------------
        \7\ 47 U.S.C. Sec. 230(c)(1) (1996).
    ---------------------------------------------------------------------------
                                     ______

          Response to Written Question Submitted by Hon. Ted Cruz to
                                   Ryan Calo
    Take It Down Act
        In my opening statement, I referenced how artificial intelligence
    (AI) can be used for nefarious purposes and how Congress should pursue
    targeted, tailored solutions that address specific harms or issues.
    It's why Sen. Klobuchar and I introduced the Take It Down Act, which
    targets bad actors who use AI to create and publish fake, lifelike
    images of real people--oftentimes teenage girls--in sexually explicit
    situations. It also gives recourse for victims to get the images taken
    down.

        Question 1. Do you support the policy in the Take It Down Act and
    agree that Congress should act to stop the proliferation of revenge
    porn, including deepfake pornographic images of adults and children?
        Answer. In a word, yes. I had occasion to watch your June 26, 2024
    field hearing. Thank you and Senator Klobuchar for your efforts on
    behalf of victims. The harms of deepfakes fall disproportionately upon
    women and vulnerable populations, and not enough is being done to
    address these harms. The subject matter experts with whom I spoke
    thought the Take It Down Act could do much good. It would perhaps be
    wise to (1) define covered platforms more broadly to include websites
    that also create and curate content; (2) add a provision deterring bad
    faith complaints; and (3) require takedown within a reasonable time-
    frame rather than 48 hours.
                                     ______

       Response to Written Questions Submitted by Hon. Maria Cantwell to
                                    Amba Kak
    Data Minimization
        We know that LLMs are trained on vast quantities of data. We also
    know that data leakage--when a large language model accidentally
    reveals sensitive information, such as personal details of an
    individual--is occurring. And we know that LLMs can be attacked by bad
    actors to extract sensitive and confidential data.
        Both scenarios become more concerning when LLMs are trained on
    private data.

        Question 1. How would a strong data minimization requirement ensure
    that training data is scrubbed to remove confidential or sensitive
    information before it is used to train an LLM?
        Answer. A strong data minimization requirement would mean that AI
    developers would need to design systems in ways that mitigate the risks
    of these harms--mandating that sensitive or confidential data must only
    be included where it is strictly necessary. Moreover, given that the
    current state of LLM technology cannot guarantee against such
    unexpected (and routine) leakages in the output phase, a data
    minimization mandate would guide developers against training models on
    such sensitive data, or prompt them not to release these systems for
    public use until such privacy standards were met.

        Question 2. How can we allow individuals to have control over their
    data after it is used to train an LLM?
        Answer. Data rights are a crucial complement to the proactive
    obligations of data minimization, as they empower individuals to
    ascertain the nature and scale of commercial surveillance, and to act
    on such information to correct, order deletion, or otherwise seek
    redress if they believe any other obligations owed to them under the
    legislation have not been fulfilled.
        Currently, the only constraint on usage of any consumer data for
    training of proprietary models comes from the terms of service of those
    products, which can be changed at will, as Google and Meta recently
    did. Notable too that while European users were alerted by Meta that it
    would use publicly available posts to train its AI, American users
    received no such notification.
        With a comprehensive data privacy law, these individuals would
    have, at minimum, the ability to demand transparency around the use of
    their data. Individuals should also have a broad right to opt out of
    algorithmic decision-making that comprises ``consequential
    decisions''--defined as decisions, including ads, that may impact an
    individual's equal access to housing, employment, healthcare, and so
    on. Such algorithmic decision-making is ubiquitous today, with limited
    oversight.
    Overcollection of Data Favors Big Companies
        The current model of developing AI systems depends on immense data
    collection, which lends an outsized advantage to a few companies.
    Without Federal standards, industry has no baseline standard to follow
    on data collection.

        Question 1. How does Federal privacy legislation even the playing
    field so that companies beyond the largest ones are able to develop new
    AI systems?
        Answer. As it stands today, there is no AI without Big Tech. These
    firms already control the resources needed to play this game of scale--
    compute infrastructure, persistent data collection, and access to the
    consumer. Currently, the largest consumer technology companies such as
    Google, Microsoft, and Amazon dominate access to such compute resources
    (and other companies, as a rule, depend on them for these
    resources).\1\ This is closely related to these companies' pre-existing
    data advantage, which enables them to collect and store large amounts
    of good-quality data about billions of people via their vast market
    penetration.
    ---------------------------------------------------------------------------
        \1\ Jai Vipra and Sarah Myers West, ``Computational Power and AI,''
    AI Now Institute, September 27, 2023, https://ainowinstitute.org/
    publication/policy/compute-and-ai.
    ---------------------------------------------------------------------------
        Data minimization rules could meaningfully constrain the
    consolidation of this data advantage and in doing so it will contribute
    to a more level playing so that we see innovation by the many, not the
    few. While some make the disingenuous claim that data privacy law will
    hurt competition and smaller players, the reality is the opposite.
    Proportionality requirements can and have been built into data privacy
    proposals, including APRA and the bipartisan ADPPA so a small tech
    business is not subject to the same compliance burden as the largest
    players. More fundamentally--Big Tech has long tried to position
    privacy as meaning that our data is safest with them even as they
    extract, combine, and profile our data without limits and often against
    the public best interest. This is deceptive and data minimization rules
    actually level the playing field by hitting at first party not just
    third party surveillance.
                                     ______

       Response to Written Questions Submitted by Hon. Ben Ray Lujan to
                                    Amba Kak
        Question 1. What does your research show on how AI companies' use
    of publicly available data to train and fine-tune their AI models is
    impacting the creative sectors--including but not limited to
    independent artists, authors, journalists and musicians?
        Answer. While we don't currently focus on the impact of AI on the
    creative sectors, the concerns here follow the same non-transparent and
    often extractive dynamics we're seeing more broadly. For example,
    widespread non-transparency on how AI systems are being trained,
    extractive practices of using creative work to train AI without
    consent, or where consent has been obtained by large media publishers
    via agreements with AI companies, workers have raised ethical and
    economic concerns about the impacts of the wholesale transfer of their
    work to AI companies. The broader concern for workers in the creative
    industry but also more generally is a gradual devaluation of human
    generated work, leading to reduce opportunities for income and a
    broader degradation of the value of human labor and creativity.

        Question 2. What rights does the AI Now Institute believe all
    individuals should have with respect to the use of their public data to
    train AI?
        Answer. Data rights are a crucial complement to the proactive
    obligations of data minimization, as they empower individuals to
    ascertain the nature and scale of commercial surveillance, and to act
    on such information to correct, order deletion, or otherwise seek
    redress if they believe any other obligations owed to them under the
    legislation have not been fulfilled.
        Currently, the only constraint on usage of any consumer data for
    training of proprietary models comes from the terms of service of those
    products, which can be changed at will, as Google and Meta recently
    did. Notable too that while European users were alerted by Meta that it
    would use publicly available posts to train its AI, American users
    received no such notification.
        With a comprehensive data privacy law, these individuals would
    have, at minimum, the ability to demand transparency around the use of
    their data. Individuals should also have a broad right to opt out of
    algorithmic decision-making that comprises ``consequential
    decisions''--defined as decisions, including ads, that may impact an
    individual's equal access to housing, employment, healthcare, and so
    on. Such algorithmic decision-making is ubiquitous today, with limited
    oversight.
                                     ______

      Response to Written Questions Submitted by Hon. Raphael Warnock to
                                    Amba Kak
    Civil Rights and Antidiscrimination
        In your written testimony, you describe how AI can ``entrench and
    embed historical inequities in sensitive social domains like
    healthcare, hiring, education, housing, and criminal justice.'' \2\
    Some Federal agencies have already taken action against online
    platforms for violating Federal antidiscrimination laws.\3\
    ---------------------------------------------------------------------------
        \2\ The Need to Protect Americans' Privacy and the AI Accelerant:
    Hearing Before the Senate Committee on Commerce, Science, and
    Transportation (Statement of Amba Kak) at 13-14.
        \3\ Ariana Tobin and Ava Kofman, Facebook Finally Agrees to
    Eliminate Tool That Enabled Discriminatory Advertising, ProPublica
    (Jun. 22, 2022), https://www.propublica.org/article/facebook-doj-
    advertising-discrimination-settlement.

        Question 1. In what ways do existing Federal antidiscrimination
    laws fail to adequately protect consumers against online
    ---------------------------------------------------------------------------
    discrimination?

        Question 2. In your view, are Federal agencies adequately enforcing
    existing antidiscrimination laws against companies using AI tools? Are
    there additional steps that Federal agencies should take to enforce
    existing laws?

        Question 3. In establishing new antidiscrimination provisions in a
    Federal data privacy law, what specific provisions should Congress
    consider, and what are the key lessons from state-level and
    international regulations on similar topics?
        Answered 1, 2, and 3 together. Data privacy law can provide a
    crucial remedy to counter data-driven discrimination at the hands of AI
    tools, and should be viewed as an essential complement to, rather than
    a substitute for, Federal antidiscrimination protections. For one,
    using people's personal information to unfairly discriminate against
    them is one of the clearest cases of abuses of data and therefore falls
    squarely within scope for data privacy laws. Moreover, in an age where
    decisions impacting people's access to key opportunities and benefits
    are increasingly being outsourced to algorithmic software, specific
    protections that reflect this contemporary context and a tailored
    enforcement regime will ensure that these harms are addressed
    comprehensively.
        Given rampant opacity in the way AI systems are designed,
    provisions protecting against such algorithmic discrimination must be
    formulated to avoid parties being able to shift responsibility to
    inscrutable machine-based decisions, making sure that the burden of
    proof to show that systems are not discriminatory is on the party that
    operates the AI system. Such a legal mandate will have a ripple effect
    in terms of encouraging greater explainability and transparency in the
    way that firms design and use AI systems for decision making.
    Public Understanding of AI and Transparency
        Pew Research recently reported that ``only three-in-ten U.S. adults
    can correctly identify all six uses of AI asked about in the survey''
    and ``44 percent think they do not regularly interact with AI.'' \4\
    These numbers underscore the limited public understanding of how often
    AI is currently used, much less how consumer data may be used to train
    AI models.
    ---------------------------------------------------------------------------
        \4\ Brian Kennedy, Alec Tyson, and Emily Saks, Public Awareness of
    Artificial Intelligence in Everyday Activities, Pew Research Center
    (Feb. 15, 2023), https://www.pewresearch.org/science/2023/02/15/public-
    awareness-of-artificial-intelligence-in-everyday-activities.

        Question 1. In crafting consumer consent and notice provisions, how
    should Congress best ensure that any information provided to consumers
    is comprehensible?
        Answer. While a data privacy law should not rely on transparency
    and consent given the significant information and power asymmetries
    between firms and their consumers, robust transparency remains a key
    part of the privacy toolkit--especially to uncover where privacy harms
    might be taking place in the first place. The FTC's updated guidance on
    the ``clear and conspicuous'' standard\5\ is a useful guide when
    formulating such a transparency standard. It emphasizes that all
    disclosures related to AI should be ``unavoidable'' when using an
    electronic medium and easily understandable by ordinary consumers. This
    means that surreptitiously changing terms of service to allow for
    changed or new uses of personal data or burying disclosures in legalese
    or behind hyperlinks would likely not meet this standard. Moreover, it
    requires firms to be creative and proactive about inviting consumer
    attention to the substance of the disclosure rather than allowing them
    to comply with a checkbox form of compliance.
    ---------------------------------------------------------------------------
        \5\ https://www.federalregister.gov/documents/2023/07/26/2023-
    14795/guides-concerning-the-use-of-endorsements-and-testimonials-in-
    advertising
    ---------------------------------------------------------------------------
                                     ______

          Response to Written Question Submitted by Hon. Ted Cruz to
                                    Amba Kak
    Take It Down Act
        In my opening statement, I referenced how artificial intelligence
    (AI) can be used for nefarious purposes and how Congress should pursue
    targeted, tailored solutions that address specific harms or issues.
    It's why Sen. Klobuchar and I introduced the Take It Down Act, which
    targets bad actors who use AI to create and publish fake, lifelike
    images of real people--oftentimes teenage girls--in sexually explicit
    situations. It also gives recourse for victims to get the images taken
    down.

        Question 1. Do you support the policy in the Take It Down Act and
    agree that Congress should act to stop the proliferation of revenge
    porn, including deepfake pornographic images of adults and children?
        Answer. We support Congress taking a strong, clear stance
    penalizing those who distribute and profit from such egregious, harmful
    uses of AI, and commend Sens. Cruz and Klobuchar's leadership on this
    issue. These targeted interventions should also be complemented by
    foundational comprehensive data privacy laws that tackle the incentives
    for unrestrained surveillance of personal information that is at the
    root cause of these and other malpractices.
                                     ______

    Response to Written Question Submitted by Hon. Shelley Moore Capito to
                                    Amba Kak
        Question 1. We know that it is frighteningly easy for minors to
    bypass website age verification as they can just pick any date over age
    21 and be able to access websites or apps. What is the best solution to
    verify a user's age without infringing on their privacy?
        Answer. While this is not a specific area of research for us, we
    wholeheartedly support formulating approaches to age verification
    online with privacy as a core guiding principle. This will ensure that
    any systems put in place are not only robust and secure, but designed
    with privacy safeguards built in from the start, for e.g., minimizing
    the personal information that is required or preferring decentralized
    and ``zero-knowledge'' approaches. In addition, in this case, we view
    privacy and competition concerns are two sides of the same coin--
    enabling Big Tech platforms to require more authentication from users
    further legitimizes the aggregation of personal information and
    government ID data across databases, sedimenting their position as
    gatekeepers of content online in ways that could hurt both privacy and
    competition.
                                     ______

       Response to Written Questions Submitted by Hon. Maria Cantwell to
                                 Udbhav Tiwari
    Lack of a Federal Privacy Law is a Global Disadvantage
        137 out of 194 countries have national data privacy laws. In many
    cases, these laws serve as the foundation for countries to develop
    regulations on AI.

        Question 1. How has the United States' lack of a Federal privacy
    law hindered our global technology advantage?
        Answer. The United States' lack of a Federal privacy law has
    significantly hindered our global technology advantage in several key
    ways. Firstly, the absence of a unified national standard creates a
    fragmented regulatory environment, forcing companies to navigate a
    patchwork of state laws. This not only increases compliance costs but
    also benefits incumbent films that can afford these bureaucratic
    expenses, thereby stifling innovation from smaller entities. While
    Mozilla supports strong state laws, we believe that support is not
    mutually exclusive to the need for a strong Federal standard, which we
    have long advocated for.
        Moreover, without clear and robust privacy laws, U.S. companies
    face lengthy and costly court battles over data collection practices.
    This legal uncertainty hampers the ability of businesses to develop and
    deploy AI technologies confidently. For instance, many data collection
    practices necessary for training AI models are now mired in legal
    disputes, which may ultimately lead to deleterious effects on
    innovation, investment, and technological progress. The lack of a
    national standard leaves companies guessing about what is permissible,
    leading to a cautious approach that impedes technological advancement.
        Internationally, the U.S. increasingly finds itself at a
    competitive disadvantage. With more than 130 out of 194 countries
    having national data privacy laws, many foreign markets have clearer
    rules that facilitate stronger consumer trust. When competing abroad,
    U.S. companies often have to adjust their data practices to comply with
    foreign regulations, adding further operational complexity and cost.
    Additionally, there is a growing lack of trust in U.S. companies
    regarding privacy protections, making it harder for them to form
    partnerships and expand internationally. Countries with stringent data
    privacy laws, such as those in the European Union, may be reluctant to
    allow U.S. companies to operate within their jurisdictions due to
    perceived risks associated with weak privacy protections as evidenced
    by the much-challenged transatlantic data transfer agreement, Privacy
    Shield, or may subject companies to heavy regulatory scrutiny, adding
    to compliance and legal costs.
        The absence of a Federal privacy law also exacerbates the existing
    ``race to the bottom'' dynamic in data collection practices. Companies,
    driven by competitive pressures, over-collect data, risking privacy
    violations and eroding consumer trust. This not only harms individuals
    but also damages the reputation of the U.S. technology sector globally.
        In sum, a comprehensive Federal privacy law is essential to
    establish clear ``rules of the road'' for data practices, foster
    innovation, and maintain the U.S.'s leadership in the global technology
    arena. By promoting data minimization and responsible data use, such a
    law would enhance consumer trust and create a more predictable and
    stable environment for technological advancement.
    Privacy for Responsible AI Innovation
        We know from experience that when companies do not have bright
    lines to follow, businesses can compete in a race to the bottom--in
    this case to over-collection of data that violates our privacy.

        Question 1. How would a bright line in a national privacy standard
    help companies innovate?
        Answer. A bright line in a national privacy standard would
    substantially enhance companies' ability to innovate by providing
    clear, consistent guidelines on data collection and usage. Without a
    unified Federal privacy law, businesses face a fragmented regulatory
    landscape, where they must navigate varying state laws, leading to
    increased compliance costs and operational inefficiencies. This
    patchwork approach benefits larger incumbents that can afford these
    expenses, while smaller companies struggle to keep pace.
        A national privacy standard would serve to eliminate the ``race to
    the bottom'' dynamics currently observed, where companies compete by
    over-collecting data to gain a competitive edge. Clear, bright-line
    rules would define acceptable data collection practices, thereby
    reducing the likelihood of privacy violations and the resultant legal
    battles, benefiting both consumers and smaller companies. This
    certainty is crucial for businesses focused on executing innovative
    strategies rather than being bogged down by legal uncertainties and
    potential lawsuits.
        For AI companies, which rely heavily on vast amounts of data to
    train and fine-tune their models, a clear national standard would
    provide necessary legal clarity. Companies would know precisely what
    data practices are permissible, fostering an environment conducive to
    responsible innovation. This would minimize the risk of expensive court
    battles and regulatory scrutiny, allowing companies to invest more
    confidently in research and development.
        Furthermore, a national privacy standard would bolster consumer
    trust. Informed consent and transparency about data use are critical
    components of such a standard. When consumers are assured that their
    data is handled responsibly and their privacy is protected, they are
    more likely to engage with new technologies. This increased engagement
    drives the adoption of innovative products and services, fueling
    further innovation.
        In summary, a bright line in a national privacy standard would
    provide the regulatory clarity needed to streamline compliance, reduce
    legal risks, and foster an environment where innovation can thrive. It
    would help to level the playing field, allowing both large and small
    companies to focus on advancing technology while safeguarding consumer
    rights, thereby driving the next wave of technological progress.

        Question 2. As AI continues to develop, how do you foresee the
    future of privacy if we do not provide bright lines about data
    collection and use--and responsible innovation?
        Answer. As AI continues to develop, the future of privacy looks
    increasingly precarious without clear, stringent guidelines on data
    collection and use. Without these ``bright lines,'' several concerning
    scenarios could unfold.
        Firstly, the absence of clear regulations frequently leads to
    rampant over-collection of personal data. Companies driven by
    competitive pressures may try to collect as much data as possible to
    fuel their AI models. This practice not only violates privacy but also
    undermines consumer trust. The potential for data leaks and misuse
    increases exponentially as more data is collected, often without
    adequate safeguards, leading to untold and unnecessary consumer harms.
        Moreover, the lack of explicit legal boundaries can result in
    prolonged legal battles as courts become the main battleground for
    defining acceptable data practices. This uncertainty can create a
    chilling effect on innovation, where businesses are hesitant to develop
    new technologies for fear of future legal repercussions. It could also
    take years for a relevant court case to ultimately make its way to the
    Supreme Court to resolve these issues definitively, leading to years of
    uncertainty and high legal costs for companies attempting to navigate
    the ambiguous regulatory landscape, with much of these costs eventually
    passed on to consumers.
        Additionally, without clear guidelines, there is little to no
    incentive for companies, even those who desire to engage in better
    privacy practices, to invest in privacy-enhancing technologies (PETs).
    The development and adoption of PETs, such as differential privacy and
    federated learning, are critical for protecting user data while
    enabling innovative uses of AI. The lack of regulatory pressure to
    adopt these technologies can lead to invasive business practices and
    further consumer harm. Relying on the voluntary adoption of privacy
    preserving practices is an incomplete solution. Ultimately, privacy,
    security, and consumer data protection practices are most effective
    when paired with robust policies based on a comprehensive framework of
    protections.
                                     ______

       Response to Written Questions Submitted by Hon. Ben Ray Lujan to
                                 Udbhav Tiwari
        Question 1. What does your research show on how AI companies' use
    of publicly available data to train and fine-tune their AI models is
    impacting the creative sectors--including but not limited to
    independent artists, authors, journalists and musicians?
        Answer. While Mozilla has not conducted extensive research on the
    impact of AI's impact on the creative sector specifically, what we have
    observed in the AI field thus far indicates that the use of publicly
    available data by AI companies to train and fine-tune their models has
    significant impacts on creatives, including independent artists,
    authors, journalists, and musicians.
        Firstly, many creatives have reported that their works are being
    used without consent to train AI models, leading to a loss of control
    over their intellectual property--often by ignoring existing mechanisms
    like the Robots Exclusion Protocol (through robots.txt files), which
    indicates to web crawlers which portions of web pages they are
    permitted to access and long served as a foundational tool of the
    modern web--but which is now proving insufficient to protect online
    data in the face of rampant collection practices by AI companies. This
    issue is exacerbated by the lack of transparency in how these datasets
    are compiled, often without the knowledge or permission of the creators
    whose works are included. For example, the AI-generated art market has
    seen substantial growth, but this has led to widespread concerns about
    copyright infringement and the ethical use of artistic works,
    especially when end users can request art in the style of certain
    artists whose work was used to train AI models, often without the
    artists knowledge or consent.
        Moreover, the economic implications for creatives are profound. As
    AI models become capable of generating high-quality content,
    indistinguishable in many cases from human-created works, there is a
    growing fear among creators of being displaced. Independent artists,
    authors, and musicians find themselves competing against AI-generated
    content, which can be produced at a fraction of the cost and time it
    takes a human creator. This competition can lead to reduced income
    opportunities and potentially undermine the value of human creativity.
        Additionally, the rise of AI in journalism has led to concerns
    about the quality and integrity of news. Automated systems can generate
    articles and reports, but they often lack the nuanced understanding and
    investigative depth that human journalists provide.
        In summary, while AI offers potential benefits in terms of
    efficiency and new forms of creativity, potentially unlocking access to
    artistic expression for those without the expertise to create music or
    art without AI tools, current practices regarding the use of publicly
    available data by the AI industry--including the legal uncertainty
    surrounding the question of rights holders' consent--pose significant
    challenges to the creative industries. Ethical considerations and
    robust policies are needed to protect the rights and livelihoods of
    independent creators and ensure that AI development progresses
    responsibly.

        Question 2. What rights does Mozilla believe all individuals should
    have with respect to the use of their public data to train AI?
        Answer. Mozilla firmly believes that individuals should have robust
    rights regarding the use of their public data to train AI models. These
    rights are fundamental to ensuring transparency, consent, and trust in
    the rapidly evolving AI landscape.
        First and foremost, Mozilla advocates for the principle of informed
    consent. Individuals should be meaningfully informed about how their
    data will be used and should have the opportunity to provide explicit
    consent before their data is used to train AI models. This transparency
    empowers users to make knowledgeable decisions about their personal
    information. It is not enough to bury consent in lengthy terms and
    conditions; clear, concise, and easily understandable information to
    laypersons should be provided.
        Secondly, Mozilla supports the right to opt-out. If consent is not
    initially obtained, individuals should have the ability to opt-out of
    having their data used for AI training at any time, through a process
    that is straightforward and user-friendly. This right ensures that
    individuals retain control over their personal data and can withdraw it
    from use if they choose.
        Additionally, individuals should have the right to access and
    correct their data. This means that people can review what data has
    been collected about them, understand how it is being used, and request
    corrections to any inaccuracies. This transparency and accuracy are
    crucial for maintaining trust and ensuring that AI models are based on
    reliable data.
        Moreover, Mozilla believes in the right to data minimization. Only
    the data necessary for specific, legitimate purposes should be
    collected and used. This principle reduces the risk of privacy breaches
    and limits the potential for misuse of personal information. It aligns
    with Mozilla's broader advocacy for privacy by design, ensuring that
    privacy considerations are integrated into AI development from the
    outset.

        Question 3. What do you believe generative AI's impact will be on
    the data broker industry?
        Answer. Generative AI's impact on the data broker industry is
    poised to be substantial, driving significant changes in data
    collection, utilization, and revenue streams. Initially, the rise of
    generative AI has created a surge in demand for large datasets. As AI
    companies rush to amass the vast amounts of data needed to train and
    fine-tune their models, data brokers find themselves in a lucrative
    position with access to data now in high demand.
        However, this growth comes with severe long-term implications. The
    widespread use of generative AI for data collection raises concerns
    about privacy and the ethical use of personal information. Data
    brokers, traditionally operating with limited oversight, may face
    increased scrutiny as the public and regulators become more aware of
    how personal data is being utilized in AI development. The Protecting
    Americans' Data from Foreign Adversaries Act of 2024, which prohibits
    the sale of Americans' personal and sensitive data to foreign
    adversaries, and California's DELETE Act are examples of steps towards
    addressing these concerns but highlights the need for broader
    regulation.

        Question 4. On April 24th, President Biden signed into law the
    Protecting Americans' Data from Foreign Adversaries Act of 2024--which
    prohibits data brokers from selling, licensing or transferring
    Americans' personal and sensitive data to foreign adversaries.

        a. Do you believe that this step puts adequate guardrails in place
    for data Brokers?

        i. If not, what additional rights and protections do you believe
    Americans deserve with respect to the sale and transfer of their
    personal data by data brokers? Please refer to any specific legislation
    or parts of legislation that Mozilla believes are strong policy
    proposals.
        Answer. The Protecting Americans' Data from Foreign Adversaries Act
    of 2024 is a significant step towards safeguarding American personal
    data from being accessed by foreign adversaries. While it addresses a
    critical national security concern, it does not comprehensively protect
    Americans' personal data in all contexts.
        Firstly, the Act's focus on foreign adversaries leaves substantial
    gaps in data protection within the U.S. It does not address the broader
    issue of data brokers selling personal data to domestic entities or
    foreign entities not classified as adversaries or the potential for the
    use of intermediaries who would sell data on to those adversaries. This
    means that Americans' sensitive data can still be extensively traded
    and used within a complex web of transactions that ultimately
    undermines privacy. Data that is collected can be extremely sensitive,
    including unprotected health data and geolocation data which may lead
    to individual privacy and safety risks. Mozilla has previously
    applauded previous Federal efforts to take on the Data Broker
    Ecosystem. In addition, the act has a somewhat more narrow definition
    of what a data broker is than some existing state laws which more fully
    cover the spectrum of the data brokerage industry.
        Mozilla believes additional protections, including the creation of
    public data broker registries, are necessary to fully safeguard
    Americans' personal data. One crucial step is the establishment of
    comprehensive Federal privacy legislation. Such legislation should
    encompass several key rights and protections including:

      1.  Right to Informed Consent: Individuals should have clear,
            comprehensible information about how their data is collected,
            used, and shared. Explicit consent should be obtained before
            any data transaction.

      2.  Right to Opt-Out: Individuals should be able to opt-out of data
            collection and sharing practices easily and at any time,
            without facing barriers or penalties.

      3.  Right to Access and Correction: Individuals should have the right
            to access the data collected about them and correct any
            inaccuracies, ensuring that their data is up-to-date and
            accurate.

      4.  Data Minimization: Only data necessary for specific, legitimate
            purposes should be collected and retained. This reduces the
            risk of misuse and enhances overall data security.

      5.  Transparency and Accountability: Companies should be required to
            provide transparency reports detailing their data practices and
            be held accountable for violations of privacy standards.
                                     ______

      Response to Written Questions Submitted by Hon. Raphael Warnock to
                                 Udbhav Tiwari
    Civil Rights and Antidiscrimination
        In your written testimony, you describe how AI can ``infer
    sensitive attributes about individuals, leading to potential privacy
    violations if used for targeted content or discrimination.'' Some
    Federal agencies have already taken action against online platforms for
    violating Federal anti discrimination laws.

        Question 1. In what ways do existing Federal antidiscrimination
    laws fail to adequately protect consumers against online
    discrimination?
        Answer. Existing Federal anti-discrimination laws, while
    foundational, fail to adequately protect consumers against the unique
    challenges posed by online discrimination, particularly when it comes
    to the use of artificial intelligence (AI) and automated systems.
        Firstly, traditional anti-discrimination laws like the Civil Rights
    Act of 1964 and the Fair Housing Act were crafted long before the
    advent of AI and digital platforms. These laws do not explicitly
    address the complexities of algorithmic bias and discrimination that
    can occur in online environments. AI systems, when trained on biased
    datasets, can inadvertently perpetuate and even amplify existing
    societal biases in ways that are extremely difficult to detect or for
    auditors to demonstrate disparate impact resulting from these
    algorithms. This can lead to discriminatory outcomes in various domains
    such as employment, lending, and housing, where automated decision-
    making tools are increasingly used.
        Furthermore, enforcement of existing laws by Federal agencies such
    as the Federal Trade Commission (FTC) and the Department of Justice
    (DOJ) has been challenging due to the opaque nature of AI algorithms.
    These agencies often lack the necessary resources and technical
    expertise to effectively scrutinize and regulate AI-driven
    discrimination.
        Although the FTC has issued guidelines and warnings about AI fraud
    and discrimination, these measures are not sufficient given the rapid
    pace of AI development and deployment.
        The limitations of current laws are exacerbated by the lack of
    transparency in how AI systems make decisions. Without clear guidelines
    on algorithmic accountability and transparency, it is difficult to
    identify and rectify instances of discrimination. Consumers often have
    no way to understand how decisions affecting them are made or to
    contest these decisions. This can prove critical when AI systems
    increasingly control important aspects of individual's lives including
    access to housing, financial resources, and even their livelihoods
    through gig platforms.
        Moreover, existing laws do not further enable more proactive
    measures such as bias audits or impact assessments for AI systems. Such
    provisions are crucial for identifying and mitigating discriminatory
    practices before they cause harm. Lessons can be drawn from state-level
    regulations and international frameworks that have started to
    incorporate requirements for bias audits and impact assessments.

        Question 2. In your view, are Federal agencies adequately enforcing
    existing anti-discrimination laws against companies using AI tools? Are
    there additional steps that Federal agencies should take to enforce
    existing laws?
        Answer. Federal agencies such as the Federal Trade Commission
    (FTC), the Equal Employment Opportunity Commission (EEOC), and the
    Department of Justice (DOJ) have made notable efforts to enforce
    existing anti-discrimination laws against companies using AI tools
    including through the EEOC's AI technical assistance document, but
    these efforts are not yet adequate given the rapid and complex
    developments in AI technology.
        The FTC has issued guidelines and warnings concerning AI fraud and
    discriminatory practices, emphasizing the need for fairness and
    transparency in AI applications. However, the agency's current
    resources and technical expertise are often insufficient to keep pace
    with the sophisticated and opaque nature of AI algorithms and
    exponential growth of the use of AI across myriad applications. The
    FTC's ability to conduct in-depth investigations and enforce compliance
    is limited by these constraints, which hinders its effectiveness in
    addressing AI-driven discrimination comprehensively.
        Similarly, the DOJ has taken steps to address discrimination
    through various initiatives and enforcement actions. However, the DOJ's
    approach to AI-related discrimination is broadly reactive rather than
    proactive. The current legal framework does not mandate regular audits
    or assessments of AI systems for bias, leaving significant gaps in
    preventive measures.
        Mozilla has studied the potential for Accelerating Progress Towards
    Trustworthy AI and released a report on the subject in early 2024. In
    addition to the insights detailed in the report, we recommend that to
    enhance the enforcement of existing laws, Federal agencies should
    consider the following additional steps:

      1.  Increase Technical Expertise and Resources: Agencies need to
            invest in technical expertise to better understand and
            scrutinize AI algorithms. This includes hiring data scientists
            and AI specialists who can identify and mitigate bias in AI
            systems.

      2.  Bias Audits and Impact Assessments: Legislation should push
            companies in high-risk sectors to conduct regular bias audits
            and impact assessments of their AI tools. These audits should
            be transparent and results should be publicly accessible to
            ensure accountability.

      3.  Enhance Transparency Requirements: Certain companies in high-risk
            sectors using AI should be required to disclose how their
            algorithms make decisions, including the data sources and
            methodologies used. This transparency will enable both
            consumers and regulators to identify potential biases and
            discriminatory practices.

      4.  Collaboration with State and International Bodies: Learning from
            existing state-level regulations and international frameworks,
            such as the GDPR in Europe, can provide valuable insights into
            effective AI governance and enforcement practices, as well as
            what to avoid.

        Question 3. In establishing new anti discrimination provisions in a
    Federal data privacy law, what specific provisions should Congress
    consider, and what are the key lessons from state-level and
    international regulations on similar topics?
        Answer. Federal agencies such as the Federal Trade Commission (FTC)
    and the Department of Justice (DOJ) have made notable efforts to
    enforce existing anti discrimination laws against companies using AI
    tools, but these efforts are not yet adequate given the rapid and
    complex developments in AI technology.
        The FTC has issued guidelines and warnings concerning AI fraud and
    discriminatory practices, emphasizing the need for fairness and
    transparency in AI applications. However, the agency's current
    resources and technical expertise are often insufficient to keep pace
    with the sophisticated and opaque nature of AI algorithms. The FTC's
    ability to conduct in-depth investigations and enforce compliance is
    limited by these constraints, which hinders its effectiveness in
    addressing AI-driven discrimination comprehensively.
        To enhance the enforcement of existing laws, Federal agencies
    should consider the following additional steps:

      1.  Increase Technical Expertise and Resources: Agencies need to
            invest in technical expertise to better understand and
            scrutinize AI algorithms. This includes hiring data scientists
            and AI specialists who can conduct in-depth technical analyses
            into AI systems and corporate claims regarding AI.

      2.  Enabling Bias Audits and Impact Assessments: As discussed in
            depth in Mozilla's comments on AI accountability to NTIA,
            legislation should better enable the audits of companies in
            sensitive industries and which develop products critical to
            individuals' welfare, such as financial and healthcare tools,
            allowing for the conducting of regular bias audits and impact
            assessments of their AI tools. These audits should be
            transparent and results should be publicly accessible to ensure
            accountability.

      3.  Enhance Transparency Requirements: Certain companies in high-risk
            sectors using AI should be required to disclose how their
            algorithms make decisions, including the data sources and
            methodologies used. This transparency will enable consumers and
            regulators to identify potential biases and discriminatory
            practices.

      4.  Collaboration with State and International Bodies: Learning from
            state-level regulations and international frameworks, such as
            the GDPR in Europe, can provide valuable insights into
            effective AI governance and enforcement practices. Closely
            monitoring the implementation and efficacy of the EU's recently
            enacted AI Act, which puts forward several measures aiming to
            curb algorithmic discrimination and to protect the fundamental
            rights of people residing in the EU, is likely to prove
            worthwhile for regulators.
    Targeting and Election Misinformation
        In your written testimony, you discuss how generative AI ``has led
    to advertisers created highly customized campaigns'' to influence
    ``unsuspecting'' consumers. Similar technologies can also be applied to
    influence unsuspecting voters, and we have already seen examples of how
    foreign actors have used generative AI to influence American
    policymaking.

        Question 4. In crafting data privacy legislation, how can Congress
    help ensure that consumers' data is not unknowingly used against them
    in crafting influence campaigns, whether to influence purchase or
    voting behavior?
        Answer. Congress can take several key steps to ensure that
    consumers' data is not unknowingly used against them in the crafting of
    influence campaigns, whether for purchasing behavior or voting
    decisions.
        Firstly, establishing comprehensive Federal privacy legislation is
    essential. Such legislation should mandate clear and explicit consent
    requirements for data collection and use and consumers must be fully
    informed about how their data will be used. This transparency will
    empower individuals to make informed decisions about their data. For
    instance, provisions similar to the General Data Protection Regulation
    (GDPR) in the European Union could be adopted, which include stringent
    consent requirements and give individuals the right to know how their
    data is being processed.
        Secondly, data minimization principles should be a cornerstone of
    any Federal privacy legislation. By limiting the amount of data
    collected to what is strictly necessary for the specified purpose, the
    risks of data misuse in influence campaigns can be significantly
    reduced. This approach not only protects consumer privacy but also
    aligns with responsible data stewardship practices advocated by
    Mozilla.
        Thirdly, implementing robust transparency and accountability
    mechanisms is crucial. Companies should be required to disclose how
    consumer data is collected, processed, and utilized, particularly in
    relation to influence campaigns. This includes revealing the sources of
    data used for targeted advertisements and influence operations.
    Enhanced transparency will enable consumers to better understand and
    control their data, reducing the likelihood of it being used against
    them without their knowledge.
        Moreover, strong enforcement and oversight mechanisms must be
    established. Regulatory bodies should be empowered to audit and review
    companies' data practices, ensuring compliance with privacy laws.
    Penalties for violations should be significant enough to deter misuse
    of consumer data. The Federal Trade Commission (FTC) and other relevant
    agencies should be given the resources and authority needed to
    effectively monitor and enforce these regulations.
        Additionally, promoting the development and adoption of privacy-
    enhancing technologies (PETs) can play a critical role in safeguarding
    users. Technologies such as differential privacy, which allows data to
    be used for analysis without compromising individual privacy, can help
    protect consumers from undue influence. By incentivizing the use of
    PETs, Congress can help ensure that consumer data is handled in a
    manner that respects privacy and reduces the risk of exploitation.
        Finally, requiring clear labeling and disclosure of data sources
    and AI in political advertisements and marketing campaigns can further
    safeguard against misuse. Consumers have the right to know how their
    data has been used to target them and the origins of the information
    influencing their decisions.

        Question 5. What requirements or best practices should Congress put
    in place to prevent foreign entities or other bad actors from using
    technology tools and platforms, including but not limited to generative
    AI, to create and spread election misinformation?
        Answer. Former Mozilla senior fellow Renee DiResta, who during her
    fellowship focused on media, misinformation, and trust, has discussed
    how generative AI ``takes the cost of creation to zero,'' which allows
    practically anyone to create compelling content.\1\ However, actors
    seeking to spread misinformation still depend on platforms to
    distribute AI generated content. To prevent foreign entities or other
    bad actors from using technology tools and platforms, including
    generative AI, to create and spread election misinformation, Congress
    should implement a series of stringent requirements and best practices.
    ---------------------------------------------------------------------------
        \1\ Interview--Online manipulation expert Renee DiResta:
    `Conspiracy theories shape our politics in extremely mainstream ways'',
    Alex Hern. The Guardian. 14 July 2024. Available here: https:/
    www.theguardian.com/technology/article/2024/jul/14/renee-diresta-
    invisble-rulers-internet-algorithms-media-disinformation-ai

      1.  Comprehensive Federal Privacy Legislation: Establish robust
            privacy laws that restrict the collection, storage, and misuse
            of personal data. Such legislation should include strict
            consent requirements and data minimization principles to ensure
            that only necessary data is collected and used responsibly.
            These measures would reduce the amount of data available for
    ---------------------------------------------------------------------------
            misuse in disinformation campaigns by foreign entities.

      2.  Enhanced Transparency Requirements: Mandate that social media
            platforms and AI developers disclose the origins of content,
            particularly political advertisements and information related
            to elections. This should include clear labeling of content
            generated, influenced, or paid for by foreign entities.
            Platforms should provide transparency reports detailing the
            measures taken to prevent the spread of misinformation.

      3.  Researcher Access for Platforms: Require technology platforms to
            provide access to relevant internal data sources. This will
            help not only create transparency, but also create
            accountability and allow for a scientific understanding related
            to potential harms caused or enabled by platforms. This should
            include the mandatory sharing of critical platform data with
            researchers and academics (with adequate privacy protections)
            to allow for the independent scrutiny of platform practices.
            Proposals like the Platform Accountability and Transparency Act
            (PATA) would enable such transparency and a better
            understanding of what's occurring on platforms. It is for this
            reason that Mozilla has led the call for Meta to continue
            supporting CrowdTangle, at least through 2024, to ensure access
            to critical data for academics, journalists, and watchdogs.

      4.  Development and Use of AI for Detection: Encourage and support
            the development of AI tools specifically designed to detect and
            mitigate the spread of misinformation. These tools can analyze
            content patterns, flag suspicious activities, and identify
            deepfakes and other forms of AI-generated misinformation.

      5.  Funding and Support for Research: Provide funding for research
            into the methods used by foreign entities to spread
            misinformation. This research can inform the development of new
            technologies and strategies to combat these efforts.

      6.  Public Awareness Campaigns: Launch educational initiatives to
            inform the public about the dangers of misinformation and how
            to identify it. Increasing public awareness can reduce the
            effectiveness of misinformation campaigns by promoting critical
            thinking and media literacy.

      7.  Legislative Measures Similar to the Protecting Americans' Data
            from Foreign Adversaries Act: Enact additional legislation
            targeting the specific practices used by foreign entities to
            spread misinformation. This includes measures to prevent the
            sale and transfer of personal data to foreign adversaries and
            to enhance cybersecurity protocols.
    Public Understanding of AI and Transparency
        Pew Research recently reported that ``only three-in-ten U.S. adults
    can correctly identify all six uses of AI asked about in the survey''
    and ``44 percent think they do not regularly interact with AI.'' These
    numbers underscore the limited public understanding of how often AI is
    currently used, much less how consumer data may be used to train AI
    models.

        Question 6. In crafting consumer consent and notice provisions, how
    should Congress best ensure that any information provided to consumers
    is comprehensible given the current limited understanding of AI and
    data privacy policies?
        Answer. In crafting consumer consent and notice provisions,
    Congress must ensure that the information provided to consumers is both
    comprehensible and accessible, addressing the current limited and
    evolving understanding of AI and data privacy policies via Federal
    privacy legislation. To achieve this, several key strategies should be
    implemented:

      1.  Plain Language Requirement: Legal and technical jargon often
            renders privacy policies incomprehensible to the average
            consumer. Congress should mandate that all consumer notices and
            consent forms be written in plain, straightforward language.
            This approach ensures that consumers can easily understand the
            terms of data use without requiring specialized knowledge.

      2.  Standardized Formats: To facilitate better understanding and
            comparison, Congress should standardize the format of privacy
            notices. This could include the use of clear headings, bullet
            points, and summaries that highlight the most critical
            information upfront as well as standardizing the timing of when
            privacy notices are shown to users--ideally right before they
            start to use a tool or website. In addition, creating a
            standardized navigation format with a functional table of
            contents to enable users to easily find specific information is
            critical to ensuring better understanding among users. A
            standardized format can help consumers quickly grasp the
            essentials of what they are consenting to.

      3.  Layered Notices: Implementing layered notices can improve
            comprehension by presenting information in manageable chunks.
            The initial layer should provide the most important information
            concisely, with additional layers offering more detailed
            explanations. This approach allows consumers to obtain a quick
            overview while retaining the option to delve deeper into the
            specifics as needed.

      4.  Visual Aids and Interactive Elements: Incorporating visual aids
            such as icons, infographics, and interactive elements can help
            convey complex information more effectively. These tools can
            simplify the presentation of data practices and consent
            options, making it easier for consumers, including those with
            low literacy skills, to understand and make informed decisions.

      5.  Accessibility Standards: Notices and consent forms must be
            accessible to all individuals, including those with
            disabilities. This includes ensuring compatibility with screen
            readers, providing text alternatives for visual content, and
            using accessible fonts and color contrasts.
                                     ______

          Response to Written Question Submitted by Hon. Ted Cruz to
                                 Udbhav Tiwari
    Take It Down Act
        In my opening statement, I referenced how artificial intelligence
    (AI) can be used for nefarious purposes and how Congress should pursue
    targeted, tailored solutions that address specific harms or issues.
    It's why Sen. Klobuchar and I introduced the Take It Down Act, which
    targets bad actors who use AI to create and publish fake, lifelike
    images of real people--oftentimes teenage girls--in sexually explicit
    situations. It also gives recourse for victims to get the images taken
    down.

        Question 1. Do you support the policy in the Take It Down Act and
    agree that Congress should act to stop the proliferation of revenge
    porn, including deep fake pornographic images of adults and children?
        Answer. We're extremely supportive of the goals of the legislation
    and making sure that vulnerable people, especially minors, aren't
    harmed by non-consensual intimate imagery (NCII) and have ways to fight
    back. We commend Sens. Cruz and Klobuchar's leadership on this issue
    and agree that Congress should act to help mitigate the terrible harms
    caused by NCII. In addition, we believe that Congress and tech
    companies can have further impact by investing in research and
    development of open-source tools to identify and combat AI generated
    content, like non-consensual deep fakes.
                                     ______

    Response to Written Questions Submitted by Hon. Shelley Moore Capito to

                                 Udbhav Tiwari
        Question 1. In your testimony you covered the importance of
    investing in privacy-enhancing technologies and how they help reduce
    risk while also allowing for innovation. How can Congress ensure that
    small businesses have equal access to these technologies?
        Answer. In our testimony, Mozilla emphasized the critical role that
    privacy-enhancing technologies (PETs) play in reducing risks and
    fostering innovation. To ensure that small businesses have equal access
    to these technologies, Congress can take several strategic steps:

      1.  Public Research Funding and Grants: Congress should allocate
            public research funding specifically for the development and
            deployment of PETs. This funding should come with stipulations
            that the research outputs are made publicly available, thereby
            ensuring that small businesses can benefit from cutting-edge
            advancements without incurring prohibitive costs. Grants and
            subsidies targeted at small and medium-sized enterprises (SMEs)
            can further support their adoption of PETs.

      2.  Support for Open-Source Projects: Encouraging and funding open-
            source projects can significantly enhance the accessibility of
            PETs. Open-source software proliferates quickly among small
            businesses due to minimal costs and the absence of vendor lock-
            in. By supporting initiatives that develop open-source PETs,
            Congress can help democratize access to these essential tools.
            In 2024, Mozilla brought together over 40 leading scholars and
            practitioners working on openness and AI; while focused on
            artificial intelligence rather than PETs, the event
            participants repeatedly emphasized the benefits of open-source
            for making it easier to identify potential privacy harms,
            highlighting the myriad benefits of openness.

      3.  Public-Private Partnerships: Establishing partnerships between
            government agencies, academic institutions, and private sector
            companies can foster the development and dissemination of PETs.
            These collaborations can provide small businesses with access
            to resources, expertise, and technologies that might otherwise
            be out of reach.

      4.  Tax Incentives and Credits: Offering tax incentives or credits to
            small businesses that invest in PETs can lower the financial
            barriers to adoption. These incentives can encourage SMEs to
            prioritize privacy and security in their operations without
            sacrificing their limited budgets.

      5.  Technical Assistance and Training Programs: Providing technical
            assistance and training programs can help small businesses
            understand and implement PETs effectively. Congress can support
            initiatives that offer workshops, online courses, and
            consulting services to educate SMEs about the benefits and
            applications of privacy-enhancing technologies.

      6.  Development of Public AI Infrastructure: Congress can support the
            creation of publicly accessible AI infrastructure, such as
            cloud-based platforms, that incorporate PETs. This
            infrastructure can be made available to small businesses at
            reduced costs, providing them with the tools they need to
            innovate while maintaining privacy standards.

      7.  Incentivizing Industry Standards: Encouraging the development and
            adoption of industry standards for PETs can level the playing
            field. Standards ensure that all businesses, regardless of
            size, can integrate privacy-enhancing technologies into their
            operations in a consistent and interoperable manner.

        Question 2. You discussed in your testimony the need to update
    existing protections in order to protect sensitive data from kids and
    sensitive categories like health data. We see these camera filters and
    other social media tools that are meant to engage our youth, but I
    worry about the data that is then being collected on our young people
    when they engage in these features, like their images and likenesses.
    Recently, Brazil has blocked Meta from using social media posts and
    data from minors to train its AI models. How can Congress work with
    industry to protect our young people so that they can engage with these
    features while protecting their images and likenesses?
        Answer. Beyond passing a Federal privacy law with clear rules of
    the road for the collection, storage, and use of children's data,
    Congress could work directly with industry as well as relevant civil
    society organizations to establish voluntary commitments. By using its
    convening power, Congress could create an environment where the largest
    relevant platforms would be able to work together, with Congressional
    oversight, to create guardrails and industry standards. While not
    legally binding, by working to create voluntary standards Congress
    could help to mitigate some of the current harms and ``race to the
    bottom'' dynamics that affect children's privacy today.
                                     ______

          Response to Written Questions Submitted by Hon. Ted Cruz to
                                  Morgan Reed
    Take It Down Act
        In my opening statement, I referenced how artificial intelligence
    (AI) can be used for nefarious purposes and how Congress should pursue
    targeted, tailored solutions that address specific harms or issues.
    It's why Sen. Klobuchar and I introduced the Take It Down Act, which
    targets bad actors who use AI to create and publish fake, lifelike
    images of real people--oftentimes teenage girls--in sexually explicit
    situations. It also gives recourse for victims to get the images taken
    down.

        Question 1. Do you support the policy in the Take It Down Act and
    agree that Congress should act to stop the proliferation of revenge
    porn, including deepfake pornographic images of adults and children?
        Answer. We appreciate your work on this important issue. From our
    perspective, when it comes to AI, Congress should focus first and
    foremost on observed harms and should be skeptical of calls to
    legislate in areas where harms are speculative and theoretical. So, we
    think it's entirely appropriate for you to legislate and clarify
    liability for publishing deepfake revenge porn. I think it's important
    that any legislation in this space strike a balance that deters and
    punishes bad actors, while safeguarding foundational protections in
    online marketplaces. Part of that is distinguishing adequately between
    social media platforms with public facing audiences and secure
    messaging services that should be empowered to protect individuals'
    privacy. But we hope to work with you on the right formula for
    legislation on this issue.
    Consequences of Adopting EU Regulatory Model
        During the hearing there was much discussion over how new
    regulatory burdens in the American Privacy Rights Act would even exceed
    European Union (EU) requirements and put small and medium-sized
    businesses at a comparative disadvantage against giant corporations.
    During my opening statement, I cautioned against the United States
    adopting an EU style regulatory system on data privacy and AI.

        Question 1. What does the United States lose if we go the direction
    of Europe and adopt heavy government intervention and control over our
    technology and entrepreneurs, particularly with respect to small
    businesses?
        Answer. Adopting the European Union's burdensome regulatory
    approach would significantly hurt U.S. small businesses, innovation,
    and technological competitiveness. The EU's regulatory approach
    generally imposes stringent ex ante requirements that limit businesses'
    ability to innovate and adapt to technological advancements. Adopting a
    philosophy of regulating first and asking questions later would
    disproportionately affect small businesses, many of which do not have
    the same resources as their larger counterparts to contend with legal
    or compliance challenges.
        While larger firms have compliance departments, smaller firms often
    lack the resources to hire employees dedicated solely to compliance.
    With a comparative advantage in compliance, ex ante regulation of
    emerging technologies tends to inure to the benefit of big companies,
    while small businesses' comparative advantage in adaptation and
    innovation is nullified. Accordingly, ex ante regulation of emerging
    technologies, especially prior to any level of market adoption or
    maturity, would effectively solidify and expand large businesses'
    existing market advantages in adjacent industries while subjecting
    small businesses to financial and logistical constraints that hinder
    their competitiveness.
        Although many App Association members have spent significant
    precious resources coming into compliance with the EU's General Data
    Protection Regulation (GDPR), it provides an interesting case study in
    how burdensome regulations affect innovative small business investment
    and growth. According to a 2018 study published by the U.S. National
    Bureau of Economic Research, venture capital invested in EU technology
    businesses decreased by more than 50 percent shortly after the GDPR
    came into effect.\1\ Venture capital is crucial for funding early-stage
    companies, which may struggle to scale or innovate without it.
    Replicating similar laws in the United States would dissuade venture
    capitalists from investing in small and medium-sized businesses and
    take away a key source of funding from some of the country's most
    innovative companies. I know that some policymakers are considering
    stepping into the AI services markets before they have even formed yet,
    and I think this is a bad idea. In particular, the Federal Trade
    Commission (FTC) and the United Kingdom's Competition and Markets
    Authority (CMA) are aggressively investigating and casting a shadow on
    significant investments by cloud companies into AI firms. The problem
    with these threats is that AI firms need large sums to train models,
    and they need cloud companies to make those investments because those
    are the savvy investors with the complementary resources to help AI
    services market entrants actually succeed and credibly compete with the
    giants. There is a serious risk that government agencies will kill
    emerging markets in the cradle because they do not like the firms
    making the investments.
    ---------------------------------------------------------------------------
        \1\ Jia, Jian, et al., 2018, The Short-Run Effects of GDPR on
    Technology Venture Investment, https://www.nber.org/papers/w25248.
    ---------------------------------------------------------------------------
        Finally, overly broad regulation could hurt U.S. global
    competitiveness in technology. Artificially slowing innovation through
    premature government intervention, such as the EU's GDPR and Digital
    Markets Act (DMA), will leave room for other parties, including bad
    actors, to create critical technologies and capture greater global
    market shares. While policymakers should enact regulations that protect
    businesses and consumers, such as a preemptive Federal privacy law,
    following the example set by the European Union, would decelerate
    innovation and enable foreign companies to overtake U.S. companies.

        Question 2. During the hearing, you spoke favorably over the
    positive uses that AI innovation can bring to the quality of life for
    Americans and for growing small business jobs. How would adopting an EU
    regulatory model in the United States put AI innovation at risk?
        Answer. Pursuing a similar regulatory model as the EU would slow AI
    innovation in the United States and place the AI-powered tools that
    U.S. consumers and small businesses use daily at risk. From Smart
    Yields' smart AgTech that helps farmers increase crop yield to Canned
    Spinach's AI-powered software development, U.S. businesses have
    produced AI systems that have been quickly integrated into a number of
    industries and provide significant benefits to both consumers and
    business owners alike.\2\ Under heavy regulatory burdens like those
    present in the EU, these AI advancements could be delayed or fail to
    reach their full potential. For example, if the United States were to
    enact legislation with overly strict data minimization prohibitions--
    especially if those provisions generally prohibit processing of data,
    with limited exceptions--Congress might inadvertently eliminate AI
    developers' access to the large datasets essential to training and
    improving models and prevent the introduction of innovative AI
    solutions into the market.
    ---------------------------------------------------------------------------
        \2\ ``Home: Smart Yields: Realtime Analytics & AI-Powered AgTech.''
    Smart Yields | Together We Can Farm SmarterSM, 10 Apr. 2024,
    smartyields.tech/#technology. ``Home.'' Canned Spinach, 17 Jan. 2024,
    cannedspinach.com/.
    ---------------------------------------------------------------------------
        Moreover, in order to meet the requirements of an EU-style
    regulatory model, small businesses would have to devote significant
    funding to legal and compliance measures. This would effectively limit
    their resources to build new AI products or hire personnel, decreasing
    both the number of AI-powered tools on the market and small business
    job growth. In turn, the regulation would effectively hinder
    technological growth and U.S. global competitiveness in the industry.
                                     ______

    Response to Written Questions Submitted by Hon. Shelley Moore Capito to

                                  Morgan Reed
        Question 1. In your written testimony, you discussed the potential
    for AI to improve Americans' lives through cloud computing. Along with
    being expensive, AI computing power requires a massive amount of
    energy. For example, one query on ChatGPT takes 18 times the energy of
    a Google search. Does our energy grid have the capacity to handle AI at
    the rate that these systems are excelling? What does Congress need to
    do to ensure that our grid is equipped to handle the extra burden that
    AI computing puts on it?
        Answer. The rapid advancement of AI has posed challenges to our
    current energy infrastructure due to its significant power
    requirements. As you noted, AI tools can consume more power than
    traditional digital activities, which can strain existing energy
    infrastructure. To ensure that our grid can accommodate growing demand
    from AI, policymakers should take three steps. First, policymakers
    should invest in modernizing infrastructure, including upgrading
    transmission lines, enhancing storage capabilities, and integrating
    smart grid technologies that enable providers to dynamically manage
    energy loads and optimize efficiency. Second, policymakers should
    promote the addition of renewable energy sources that can help meet
    increased energy demands of AI without exacerbating environmental
    impacts. Finally, policymakers should partner with private enterprises
    and support research and development initiatives designed to accelerate
    the advancement of more energy-efficient computing technologies, on-
    device processing, and AI systems that require less power than current
    models. Many AI businesses have released AI systems with lighter
    weights that require less energy than popular models. For example,
    OpenAI recently released GPT-4o mini, a small language model that
    requires less computational power and energy resources to run than the
    company's other recent models, yet still performs at similar
    standards.\3\
    ---------------------------------------------------------------------------
        \3\ Sophia, Deborah. ``OpenAI Unveils Cheaper Small AI Model GPT-4o
    Mini.'' Reuters, 18 July 2024, https://www.reuters.com/technology/
    artificial-intelligence/openai-unveils-cheaper-small-ai-model-gpt-4o-
    mini-2024-07-18/. Accessed 8 Aug. 2024.
    ---------------------------------------------------------------------------
        Additionally, while AI increases energy consumption, it can also
    offer solutions to enhance energy efficiency across various industries,
    reduce greenhouse gas emissions, help electricity providers optimize
    operations, create jobs in the energy market, and more.\4\ For example,
    the energy sector can use AI to monitor power flows, predict consumer
    demand, and maintain reliable grid operations.\5\
    ---------------------------------------------------------------------------
        \4\ ``It's Not Easy Being Green . . . Or Is It?'' ACT | The App
    Association, 21 Nov. 2022, https://actonline.org/2022/11/21/its-not-
    easy-being-green-or-is-it/. Accessed 8 Aug. 2024.
        \5\ Rozite, Vida, et al., ``Why AI and Energy Are the New Power
    Couple.'' International Energy Agency, 2 Nov. 2023, https://
    www.iea.org/commentaries/why-ai-and-energy-are-the-new-power-couple.
    Accessed 8 Aug. 2024.

        Question 2. The Artificial Intelligence (AI) Research, Innovation,
    and Accountability Act that I am a lead cosponsor of would establish a
    framework for greater transparency, accountability, and security for
    high-impact AI systems. This bill would require NIST to research
    standards and applications for showing authenticity and ownership of
    content published online. How important is it for people publishing
    content online to have assurances that their work will carry a mark of
    their ownership, and is that enough of a deterrent for bad actors? What
    do you believe will happen if AI continues to progress and is left
    unchecked by the Federal government?
        Answer. We appreciate your focus on appropriate regulations for AI
    systems. Enacting regulation that supports its progress will ensure
    that U.S. consumers can continue to take advantage of useful AI tools,
    support job creation and economic growth, and boost U.S. global
    technological competitiveness. However, we have concerns that premature
    or broad legislation that requires additional oversight by Federal
    agencies may impede the development of AI tools and make it more
    difficult for U.S. businesses to innovate. Moreover, legislation that
    increases regulatory burdens will have a disproportionate impact on
    small businesses, which do not have the same resources as their larger
    counterparts to come into compliance. We welcome the opportunity to
    work with you on developing legislation that achieves the same goals of
    protecting U.S. consumers while bolstering innovation.

                                      [all]