    [House Hearing, 119 Congress]
    [From the U.S. Government Publishing Office]

                      UNLOCKING THE NEXT GENERATION OF AI
                          IN THE U.S. FINANCIAL SYSTEM
                           FOR CONSUMERS, BUSINESSES,
                              AND COMPETITIVENESS
    =======================================================================

                                    HEARING

                                   BEFORE THE

                     SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL
                     TECHNOLOGY, AND ARTIFICIAL INTELLIGENCE

                                     OF THE

                        COMMITTEE ON FINANCIAL SERVICES
                         U.S. HOUSE OF REPRESENTATIVES

                        ONE HUNDRED NINETEENTH CONGRESS

                                 FIRST SESSION

                                   __________

                               SEPTEMBER 18, 2025

                                   __________

                               Serial No. 119-43

           Printed for the use of the Committee on Financial Services

    [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

                                www.govinfo.gov

                                 __________

                      U.S. GOVERNMENT PUBLISHING OFFICE
    63-432 PDF               WASHINGTON : 2026
    =======================================================================

                     HOUSE COMMITTEE ON FINANCIAL SERVICES

                        FRENCH HILL, Arkansas, Chairman

    BILL HUIZENGA, Michigan, Vice        MAXINE WATERS, California, Ranking
        Chairman                             Member
    FRANK D. LUCAS, Oklahoma             SYLVIA R. GARCIA, Texas, Vice
    PETE SESSIONS, Texas                     Ranking Member
    ANN WAGNER, Missouri                 NYDIA M. VELAZQUEZ, New York
    ANDY BARR, Kentucky                  BRAD SHERMAN, California
    ROGER WILLIAMS, Texas                GREGORY W. MEEKS, New York
    TOM EMMER, Minnesota                 DAVID SCOTT, Georgia
    BARRY LOUDERMILK, Georgia            STEPHEN F. LYNCH, Massachusetts
    WARREN DAVIDSON, Ohio                AL GREEN, Texas
    JOHN W. ROSE, Tennessee              EMANUEL CLEAVER, Missouri
    BRYAN STEIL, Wisconsin               JAMES A. HIMES, Connecticut
    WILLIAM R. TIMMONS, IV, South        BILL FOSTER, Illinois
        Carolina                         JOYCE BEATTY, Ohio
    MARLIN STUTZMAN, Indiana             JUAN VARGAS, California
    RALPH NORMAN, South Carolina         JOSH GOTTHEIMER, New Jersey
    DANIEL MEUSER, Pennsylvania          VICENTE GONZALEZ, Texas
    YOUNG KIM, California                SEAN CASTEN, Illinois
    BYRON DONALDS, Florida               AYANNA PRESSLEY, Massachusetts
    ANDREW R. GARBARINO, New York        RASHIDA TLAIB, Michigan
    SCOTT FITZGERALD, Wisconsin          RITCHIE TORRES, New York
    MIKE FLOOD, Nebraska                 NIKEMA WILLIAMS, Georgia
    MICHAEL LAWLER, New York             BRITTANY PETTERSEN, Colorado
    MONICA DE LA CRUZ, Texas             CLEO FIELDS, Louisiana
    ANDREW OGLES, Tennessee              JANELLE BYNUM, Oregon
    ZACHARY NUNN, Iowa                   SAM LICCARDO, California
    LISA McCLAIN, Michigan
    MARIA SALAZAR, Florida
    TROY DOWNING, Montana
    MIKE HARIDOPOLOS, Florida
    TIM MOORE, North Carolina

                          Ben Johnson, Staff Director

                                     ------

     SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL TECHNOLOGY, AND ARTIFICIAL
                                  INTELLIGENCE

                        BRYAN STEIL, Wisconsin, Chairman

    TOM EMMER, Minnesota, Vice Chairman  STEPHEN F. LYNCH, Massachusetts,
    BILL HUIZENGA, Michigan                  Ranking Member
    WARREN DAVIDSON, Ohio                BRAD SHERMAN, California
    JOHN W. ROSE, Tennessee              BILL FOSTER, Illinois
    WILLIAM R. TIMMONS, IV, South        JOSH GOTTHEIMER, New Jersey
        Carolina                         AYANNA PRESSLEY, Massachusetts
    MARLIN STUTZMAN, Indiana             RITCHIE TORRES, New York
    BYRON DONALDS, Florida               SYLVIA R. GARCIA, Texas
    ZACHARY NUNN, Iowa                   BRITTANY PETTERSEN, Colorado
    TROY DOWNING, Montana                SAM LICCARDO, California
    MIKE HARIDOPOLOS, Florida
    TIM MOORE, North Carolina

                            C  O  N  T  E  N  T  S

                                  ----------

                          Thursday, September 18, 2025
                               OPENING STATEMENTS

                                                                       Page
    Hon. Bryan Steil, Chairman of the Subcommittee on Digital Assets,
      Financial Technology and Inclusion, a U.S. Representative from
      Wisconsin......................................................     1
    Hon. Stephen Lynch, Ranking Member of the Subcommittee on Digital
      Assets, Financial Technology and Inclusion, a U.S.
      Representative from Massachusetts..............................     2

                                   STATEMENTS

    Hon. French Hill, Chairman of the Committee on Financial
      Services, a U.S. Representative from Arkansas..................     4

                                   WITNESSES

    Mr. Christian Lau, Co-Founder and Chief Product Officer, Dynamo
      AI.............................................................     4
        Prepared Statement...........................................     7
    Dr. David Cox, Vice President, AI Models, IBM Director, MIT-IBM
      Watson AI Lab..................................................    18
        Prepared Statement...........................................    20
    Mr. Matthew Reisman, Director, Privacy and Data Policy, Center
      For Information Policy Leadership..............................    26
        Prepared Statement...........................................    28
    Mr. Daniel Gorfine, Founder & CEO, Gattaca Horizons; Former Chief
      Innovation Officer & Director of LabCFTC.......................    30
        Prepared Statement...........................................    32
    Dr. Nicol Turner Lee, Senior Fellow and Director, Center for
      Technology Innovation, Brookings Institution...................    50
        Prepared Statement...........................................    52

                                    APPENDIX

                       MATERIALS SUBMITTED FOR THE RECORD

    Warren Davidson:
        Op-ed for the Daily Caller...................................    96
    Hon. Maxine Waters:
        America's Credit Unions......................................    99
        The National Fair Housing Alliance (NFHA)....................   103
        Public Citizen...............................................   111

                     RESPONSES TO QUESTIONS FOR THE RECORD

    Written responses to questions for the record from Mr. Christian
      Lau............................................................   113
    Written responses to questions for the record from Dr. David Cox.   116
    Written responses to questions for the record from Mr. Matthew
      Reisman........................................................   118
    Written responses to questions for the record from Dr. Nicol
      Turner Lee.....................................................   123

                                  LEGISLATION

    H.R. 4801, the Unleashing AI Innovation in Financial Services Act   127
    H.R. 2152, the Artificial Intelligence Practices, Logistics,
      Actions, and Necessities (PLAN) Act............................   148
    H.R. 1734, the Preventing Deep Fake Scams Act....................   153

                      UNLOCKING THE NEXT GENERATION OF AI
                          IN THE U.S. FINANCIAL SYSTEM
                           FOR CONSUMERS, BUSINESSES,
                              AND COMPETITIVENESS

                                  ----------

                          Thursday, September 18, 2025

                         U.S. House of Representatives,
                               Committee on Financial Services,
                                                        Washington, DC.

        The subcommittee met, pursuant to notice, at 2:12 p.m., in
    room 2128, Rayburn House Office Building, Hon. Bryan Steil
    [chairman of the subcommittee] presiding.
        Present: Representatives Steil, Hill, Huizenga, Davidson,
    Rose, Timmons, Nunn, Downing, Haridopolos, Lynch, Waters,
    Foster, Pressley, Torres, Garcia, Pettersen, and Liccardo.
        Chairman Steil. The Subcommittee on Digital Assets,
    Financial Technology, and Artificial Intelligence will come to
    order.
        Without objection, the chair is authorized to declare a
    recess of the committee at any time.
        The hearing is titled ``Unlocking the Next Generation of AI
    in the U.S. Financial System for Consumers, Businesses, and
    Competitiveness.''
        Without objection, all members will have 5 legislative days
    within which to submit additional material to the chair for
    inclusion in the record.
        I now recognize myself for 4 minutes for an opening
    statement.

        OPENING STATEMENT OF HON. BRYAN STEIL, CHAIRMAN OF THE
       SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL TECHNOLOGY AND
            INCLUSION, A U.S. REPRESENTATIVE FROM WISCONSIN

        Artificial intelligence is rapidly changing industries
    across the world. Few sectors are more prepared and more
    impacted than financial services. For decades, our financial
    institutions have been at the forefront of development and
    deploying AI technology, from algorithmic trading to machine
    learning systems used in risk management to combating fraud in
    better and faster ways.
        The rise of generative AI represents the next
    transformative step, which could bring new efficiencies, new
    opportunities, and potential new risks to our financial
    markets. This subcommittee has demonstrated strong leadership
    in charting a path forward for transformative technologies,
    including digital assets, most recently through the CLARITY Act
    and the Guiding and Establishing National Innovation for U.S.
    Stablecoins (GENIUS) Act.
        Today, we turn our attention to artificial intelligence,
    examining how it is being deployed in financial markets and
    assessing whether the current regulatory framework is prepared
    to keep pace.
        The United States has long been a hub for financial
    innovation, and we must ensure our policies support responsible
    AI adoption, not stifle it, as seen during the Biden-Harris
    Administration.
        Recently, the Trump Administration released its AI Action
    Plan, which emphasizes American AI leadership across all
    sectors. We must look to how we can enhance American leadership
    and competitiveness in financial technology.
        It is essential that regulations strike the right balance,
    fostering innovation while ensuring investor protection and
    market integrity. Our financial regulators must be cognizant of
    that technology it uses, and Congress must provide the clarity
    to encourage responsible development here at home. It is
    paramount our markets are not left behind in the global race
    for AI leadership.
        We are fortunate to have with us today a panel of esteemed
    experts who bring a wealth of knowledge and experience in
    artificial intelligence and its deployment in the financial
    system. The information we learn today will build upon our
    prior work and assist the subcommittee in shaping policies that
    encourage responsible innovation in financial markets while
    cementing American AI leadership.
        I want to thank our witnesses for being with us today, and
    I look forward to today's discussion.
        I will now recognize the ranking member of the
    subcommittee, Mr. Lynch, for 4 minutes for an opening
    statement.

    OPENING STATEMENT OF HON. STEPHEN LYNCH, RANKING MEMBER OF THE
       SUBCOMMITTEE ON DIGITAL ASSETS, FINANCIAL TECHNOLOGY AND
          INCLUSION, A U.S. REPRESENTATIVE FROM MASSACHUSETTS

        Mr. Lynch. Thank you very much, Mr. Chairman, and thank you
    for your courtesy. I had a vote on a subpoena in another
    hearing and that caused my delay but thank you.
        I want to thank this panel of witnesses for your
    willingness to come before the committee and help us with our
    work.
        This hearing continues this committee's oversight work to
    examine the use of AI in the financial services and housing
    sectors. As evidenced by the final report issued by the
    bipartisan working group that Chairman Hill and I co-chaired
    last year, financial institutions, fintech companies, and even
    financial regulators are already deploying AI to maximize
    operational efficiency and achieve cost reduction across their
    core functions, from personalized customer services and
    consumer lending to fraud detection and financial crime
    monitoring.
        At the same time, the rapid development of AI-based
    technologies has introduced serious risks into the financial
    service space. The Treasury Department, the Federal Reserve,
    and Consumer Financial Protection Bureau have all expressed
    concerns. Other financial regulators have repeatedly cautioned
    that the development of robust and trustworthy artificial
    intelligence is dependent on our ability to encourage
    innovation that maximizes oversight, consumer protection, data
    privacy, as well as workforce protection and marketplace
    fairness.
        In view of these considerations, I am concerned by the
    Trump Administration's decision to rescind some of the
    commonsense Federal directives that sought to advance the safe
    and responsible development of artificial intelligence. This
    includes the reversal of an executive order directing all
    Federal agencies to enforce existing consumer protection laws
    and to develop additional regulatory safeguards against fraud,
    unintended bias, discrimination and privacy violations only
    when absolutely necessary.
        President Trump subsequently issued his own AI Action Plan,
    a strategy that largely reflects the divergent view that
    consumer protection stands as an impediment to AI innovation, a
    position with which I strongly disagree.
        Regrettably, the AI Action Plan undermines State-level AI
    regulation by requiring Federal agencies to, quote, consider a
    State's regulatory climate, close quote, when allocating
    funding, and to outstrip Federal funds from any State with a
    regulatory framework that it considers burdensome.
        I would note that recent legislation to impose a 10-year
    moratorium on State AI regulation was soundly defeated in the
    U.S. Senate by an overwhelming bipartisan vote of 99 to 1.
        The AI Action Plan similarly impedes Federal oversight of
    AI-based systems by directing the Federal Trade Commission to
    stand down from ongoing investigations that unduly burden AI
    innovation. It is the fundamental mission of the Federal Trade
    Commission (FTC) to protect the American public from unfair or
    deceptive business practices.
        President Trump also recently issued an executive order to
    limit Federal procurement of AI large language models to those
    that are developed with ideological neutrality, as reported by
    the independent Brennan Center for Justice.
        Compliance with the order will likely require technology
    companies to degrade model performance and undermine public
    trust in their technology. Not surprisingly, our committee has
    received regular reports from AI stakeholders who have put AI
    development on hold over concerns that their integration of
    fairness metrics will run afoul of the executive order.
        Innovation will also not be well served by the dismantling
    of the Consumer Financial Protection Bureau (CFPB), a Federal
    agency that has sought to advance the development of
    trustworthy AI systems in financial services through consumer
    protection enforcement actions and regulatory clarity.
        In stark contrast to these actions, the work of our
    bipartisan AI Working Group stems from a genuine commitment by
    members on both sides of the aisle to foster AI innovation in
    the financial services industry while also ensuring that
    regulators are equipped with the authorities and resources
    necessary.
        In closing, to this end I look forward to continuing to
    work on a bipartisan basis with Chairman Hill, Chairman Steil,
    Ranking Member Waters and our committee colleagues to advance
    the U.S. leadership in this important area.
        Thank you, Mr. Chairman, for your courtesy once again, and
    I yield back the balance of my time.
        Chairman Steil. The gentleman yields back.
        I now recognize the chairman of the full committee, Mr.
    Hill, for 1 minute for an opening statement.

      STATEMENT OF HON. FRENCH HILL, CHAIRMAN OF THE COMMITTEE ON
        FINANCIAL SERVICES, A U.S. REPRESENTATIVE FROM ARKANSAS

        Chairman Hill. Thank you, Chairman Steil and I appreciate
    our panel being with us.
        AI has, of course, rapidly changed the way Americans live,
    work, and engage with our financial system. Last Congress, we
    explored AI practices at financial services firms, regulators,
    and supervisors. Congressman Bill Foster and I served on
    Speaker Johnson and Minority Leader Jeffries' Bipartisan
    congressional AI Task Force. Ms. Garcia and Mr. Lynch and I had
    an excellent work session at MIT's labs on their research in AI
    and financial services as well.
        In all these efforts, we saw demonstrated how AI has the
    potential to boost efficiency, cut costs, and strengthen the
    tools used to protect consumers from fraud detection to anti-
    money laundering but as with any innovation, there are risks.
    AI systems have to be trustworthy, fair, and secure.
        Today we will start that exploration in this Congress on
    how AI, particularly the emerging capabilities of generative
    AI, can reshape our financial system.
        I look forward to the discussion, and I yield back.
        Chairman Steil. Thank you, Mr. Chairman.
        Today we welcome an esteemed panel. We welcome the
    testimony of Dr. David Cox, vice president of AI Models at IBM
    Research; Dr. Christian Lau, co-founder and president of Dynamo
    AI; Mr. Matthew Reisman, director of privacy and data
    collection at the Center for Information Policy Leadership; and
    Daniel Gorfine, CEO of Gattaca Horizons LLC; as well as Dr.
    Nicol Turner Lee, senior fellow and director of the Center for
    Technology Innovation at the Brookings Institution.
        We thank each of you for taking your time to be here. Each
    of you will be recognized for 5 minutes to give an oral
    presentation of your testimony. Without objection, your written
    statements will be made part of the record.
        Dr. Lau, you are now recognized for 5 minutes for your oral
    remarks.

       STATEMENT OF CHRISTIAN LAU, CO-FOUNDER AND CHIEF PRODUCT
                           OFFICER, DYNAMO AI

        Mr. Lau. Chairman Steil, Ranking Member Lynch, members of
    the subcommittee, and staff, I thank you for inviting me to
    testify today, and I am honored to participate in discussions
    focused on advancing AI across the financial services
    ecosystem.
        In 2021, I founded Dynamo AI alongside my co-founder and
    CEO, Vaikkunth Mugunthan, during our Ph.D.s at MIT. We started
    off as a small group of researchers deeply interested in AI but
    also keenly aware that AI risks pose fundamental challenges to
    real world adoption.
        Since starting as a small group of Ph.D.s, we quickly found
    ourselves working hand in hand with some of the largest
    financial institutions, the most cutting-edge financial
    technology (fintech) companies, as well as regional banks
    across America to help them navigate compliance and governance
    challenges posed by AI.
        Today, we not only provide the security and governance
    layer for many of the largest deployments of AI in banking, but
    we are also now proudly backed by 40 of the top 100 U.S.
    financial institutions and a consortium of community banks
    across America.
        Our mission has always been to help enterprises navigate
    complex regulatory environments, particularly where legal and
    compliance requirements pose open technology challenges that
    institutions struggle to solve. We found that, when faced with
    new technology regulations or internal compliance requirements
    around new technologies, enterprises are often left paralyzed,
    asking themselves not only how do I comply with these new
    requirements but is it even technically possible for us to
    comply with these new requirements. Nowhere do we see this to
    be more prevalent than with the struggles of financial
    institutions striving to adopt AI.
        Every day, our team has the ability and opportunity to
    witness exciting new AI proof of concepts that bring new
    efficiencies to the bank but for every new exciting AI proof of
    concept that we encounter, we also see another AI proof of
    concept fail to make it into production and deliver meaningful
    value. We commonly see that these projects fail not because the
    underlying AI technology cannot deliver but, rather, because
    financial institutions struggle to answer open questions about
    managing AI risk in heavily regulated, high-impact, and
    consequential environments.
        Some common and, quite frankly, sensible questions about AI
    risk include: What new security and data leakage risks does AI
    bring to my organization? As we hand over more autonomy to AI
    agents, how can we enable those agents to comply with
    established banking protocol and procedure when carrying out
    tasks and what happens when an AI assistant inevitably
    hallucinates or fabricates facts in its response?
        While financial institutions often struggle to answer these
    questions, these are not intractable problems. At Dynamo, we
    work with a multitude of financial institutions to establish
    effective AI risk management that accelerates rather than
    blocks their AI transformation.
        To truly manage these new risks and unleash AI innovation,
    financial institutions and regulators must embrace technology
    solutions that can help risk and compliance teams scale their
    oversight, including controls like AI guardrails, red-teaming
    evaluations, and auditability over AI usage.
        Importantly, the AI landscape is evolving at breakneck
    speed, and regulators and policymakers need to adopt governing
    frameworks that keep up with the pace of innovation, rather
    than falling behind on new opportunities and risks.
        Regulators should expand the use of AI sandboxes, as called
    for in the administration's AI Action Plan and the bipartisan
    H.R. 4801, to not only enable technology teams to experiment
    with AI on high-impact use cases but also bring leading
    evaluation and red-teaming technology to rigorously test these
    experimental AI against real world risks.
        Drawing on global examples, such as Singapore's successful
    sandbox programs, the U.S. can strengthen competitiveness while
    promoting secure and compliant AI.
        Across financial institutions and governing bodies that we
    speak to every day, we are starting to see comprehensive AI
    risk management take shape. We believe that this will be key to
    ushering a new and exciting era of advancement for the
    industry.
        On behalf of the entire team at Dynamo AI, thank you for
    the opportunity to testify, and I welcome the opportunity to
    work with the subcommittee to create a competitive, secure, and
    compliant AI ecosystem within financial services.
        Thank you.

        [The prepared statement of Mr. Lau follows:]
        [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]

        Chairman Steil. Thank you very much.
        Dr. David Cox, you are now recognized for 5 minutes.

        STATEMENT OF DAVID COX, VICE PRESIDENT, AI MODELS; IBM
                    DIRECTOR, MIT-IBM WATSON AI LAB

        Mr. Cox. Chairman Steil, Ranking Member Lynch, and
    distinguished members of this committee, thank you for the
    opportunity to testify. My name is David Cox, and I serve as
    the vice president for AI Models at IBM Research and the IBM
    director of the MIT-IBM Watson AI Lab.
        While the excitement around AI has intensified in recent
    years, artificial intelligence has fascinated researchers for
    many decades. To give you a sense of our historical place in
    this, IBM has been on the vanguard of this ongoing revolution
    from the very beginning, when an IBM researcher co-authored the
    proposal for the 1956 workshop, Dartmouth conference, that
    coined the term ``artificial intelligence'' and gave the field
    its name.
        IBM also has a long history of helping enterprises,
    including in the financial sector, use artificial intelligence
    technologies to unlock business value, and doing so in ways
    that are responsible and engender trust.
        Our work spans a broad spectrum, from helping to identify
    appropriate use cases, to providing tooling to govern both the
    development and deployment of AI systems, to inventing new
    technologies for trustworthy AI.
        From the start, IBM has tested AI internally before
    offering it to others, an approach we call Client Zero. By
    deploying AI internally, we ensure our models are stress tested
    in the real world environments before they ever reach clients.
        For example, by leveraging IBM's watsonx.ai and automation
    tools, we help to augment the skills of our own workforce by
    eliminating repetitive tasks. This has enabled our employees to
    focus on more challenging, rewarding, and impactful work with
    the time they save.
        For the financial industry, the implications of AI,
    particularly generative AI and large language models, are
    transformative. Learning Management System (LMS) are not merely
    chatbots, they are multifunctional tools that can be adapted
    across a wide range of financial services use cases. These
    opportunities come with challenges. Large models consume
    enormous computing resources, raising both costs and concerns
    about energy use.
        Transparency is vital. Enterprises and regulators must
    understand the provenance and quality of the data underlying
    deployed systems. Security is also paramount as organizations
    seek to safeguard sensitive information when using cloud-based
    systems but perhaps the greatest risk is hesitation. If
    industry delays too long, consumers and the U.S. economy may
    miss out on the early benefits of adoption.
        Responsible governance is not a break on innovation. It is
    a mechanism that ensures that innovation can be deployed
    securely and sustainably.
        In regulated environments, firms must understand exactly
    what data underpins their models and be able to audit those
    systems over time. That is why IBM's enterprise AI efforts are
    grounded in three principles: open, trusted, and secure.
        Open-source AI strengthens transparency, produces
    dependence on proprietary vendors, and enhances U.S.
    competitiveness. Trust is built through transparency and data
    curation, training processes, and lineage between models and
    data. Finally, security must be embedded throughout the AI life
    cycle, from data collection to deployment, backed by continuous
    oversight rather than reactive compliance.
        With this in mind, I urge policymakers to support open
    ecosystems, to regulate applications rather than technologies
    in the abstract, and to promote transparency requirements that
    allow enterprises and regulators alike to test models, evaluate
    accuracy, and understand safeguards. These steps will foster an
    environment where innovation can thrive responsibly.
        AI is not about replacing people but augmenting them. It is
    about empowering professionals, enriching consumers, and
    expanding opportunity. By embracing openness, insisting on
    transparency, and embedding security, we can ensure that AI
    strengthens both the financial system and America's global
    competitiveness.
        Thank you, and I look forward to your questions.

        [The prepared statement of Mr. Cox follows:]
        [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]

        Chairman Steil. Thank you very much.
        Mr. Reisman, you are now recognized for 5 minutes.

       STATEMENT OF MATTHEW REISMAN, DIRECTOR, PRIVACY AND DATA
            POLICY, CENTER FOR INFORMATION POLICY LEADERSHIP

        Mr. Reisman. Thank you, Chairman Steil, Ranking Member
    Lynch, and members of the subcommittee, for the opportunity to
    speak with you today.
        I am Matthew Reisman, director of Privacy and Data Policy
    at the Center for Information Policy Leadership, or CIPL, a
    data and privacy policy think tank within the Hunton law firm,
    whose mission is to advance best practices for the responsible
    and beneficial use of data. CIPL facilitates constructive
    engagement between business leaders, data governance experts,
    regulators, and policymakers around the world.
        AI plays a critical role in the financial services
    industry, as this committee documented well in the staff report
    of its bipartisan Working Group on Artificial Intelligence. For
    years, machine learning has strengthened financial services
    institutions' ability to combat fraud, provide richer and more
    tailored services to existing customers, and extend services to
    new ones. More recently, generative AI has boosted productivity
    across functions, from software development to customer service
    and we are now in the early days of agentic AI, which shows
    promise for enhancing the experiences of businesses and
    customers alike. Potential applications are extensive, from
    streamlining Know Your Customer processes, to back-office
    operations like payroll and invoicing, to online banking and
    agentic commerce.
        AI's use in financial services also carries risks to
    consumers, institutions, and the financial system. The
    bipartisan staff report documents these risks well. Agentic AI
    may accentuate some risks while at the same time enhancing risk
    management capabilities in areas such as privacy and
    cybersecurity.
        To secure the advantages of AI within the financial system,
    we have three broad sets of recommendations:
        First, with respect to regulation, pursue a risk-based
    approach that focuses on the outcomes to be achieved. Avoid
    overly prescriptive measures, build upon existing foundations,
    including regulations, guidance, and standards that are already
    in place. When necessary, clarify or adapt their application to
    emerging technologies. Consider risks and benefits in equal
    measure. Incentivize organizations to adopt accountable
    practices. Build trust through meaningful transparency. CIPL
    has long underscored that these concepts of organizational
    accountability are central to smart governance of data and
    technology.
        Second, enable the responsible use of data for model
    training and development. To function safely, effectively, and
    fairly, models must be trained and tested using rich datasets.
    Regulators should apply data protection principles in ways that
    ensure the quality of AI systems while preserving individuals'
    privacy. Privacy-enhancing technologies, or PETs, can reduce
    risks associated with the use of personal data. Examples
    include synthetic data, which is artificial data that mimics
    the value of real world data, and differential privacy, where
    random noise is added to datasets to prevent identification of
    any individual's data. Policymakers should encourage continued
    research on and use of PETs.
        Third, engage in cooperative dialog among regulators,
    technologists, and industry. As AI continues to evolve rapidly,
    stakeholders must learn from each other. Fostering such
    exchanges is the heart of CIPL's mission, and regulatory
    sandboxes offer an invaluable avenue for such dialog. Numerous
    jurisdictions have established regulatory sandboxes over the
    past decade, from the U.K. to Singapore to U.S. States, like
    North Carolina and Delaware. We commend steps to promote
    sandboxes under America's AI Action Plan, the proposed
    bipartisan, bicameral Unleashing IA Innovation and Financial
    Services Act, and other proposed legislation.
        CIPL has published numerous papers on the aforementioned
    topics and is continuing our research on them.
        We look forward to the discussion today and to supporting
    your efforts to secure the benefits of AI and financial
    services for everyone. Thank you.

        [The prepared statement of Mr. Reisman follows:]
        [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]

        Chairman Steil. Thank you very much.
        Mr. Gorfine, you are now recognized for 5 minutes.

     STATEMENT OF DANIEL GORFINE, FOUNDER & CEO, GATTACA HORIZONS;
         FORMER CHIEF INNOVATION OFFICER & DIRECTOR OF LABCFTC

        Mr. Gorfine. Thank you, Chairman Steil, Ranking Member
    Lynch, and members of the subcommittee, for the opportunity to
    testify before you today.
        I am the founder and CEO of Gattaca Horizons, an adjunct
    professor at the Georgetown University Law Center, and the
    former chief innovation officer at the U.S. Commodity Futures
    Trading Commission (CFTC).
        Today's topic on unlocking the next generation of AI in our
    financial system is critically important. The U.S. holds
    significant competitive and first mover advantages, and we must
    foster continued development through thoughtful policy
    approaches. AI presents tremendous opportunities to further
    expand access, lower costs, and increase efficiencies and
    competitiveness while also enhancing compliance and regulatory
    oversight.
        To level set, it is important to recognize that AI and
    financial services are not new. It is part of a steady
    progression of automation that began decades ago. Today, recent
    advances in generative AI and agentic AI are creating new
    possibilities, ranging from generating code and new content to
    planning and executing complex tasks.
        Responsibly developed AI is, indeed, already yielding
    tremendous benefits. AI can provide more accurate and efficient
    decisionmaking, detect patterns that traditional approaches
    would miss, help regulators keep pace with digital markets, and
    promote financial inclusion by unlocking credit for
    historically underserved populations. It is further enhancing
    customer service, helping to identify patterns of financial
    crime and market manipulation, and making financial advice more
    accessible and lower cost for Americans.
        As with any area of innovation, however, there are risks
    associated with AI, including the potential for perpetuating
    bias, relying on poor quality data, failing to operate as
    expected, and advancing fraud and scams. The mere speculative
    potential or fear of future harm, however, should not broadly
    block development of AI, including by those small firms and
    community banks seeking to remain competitive in an
    increasingly digital economy.
        To this end, as a key guiding principle, I would encourage
    everyone to assess new AI-based models on their ability to
    improve off of a highly imperfect status quo. This principle
    should apply across AI applications, since a singular focus on
    risk can blind us to the greater benefits as compared to legacy
    approaches.
        With respect to existing policy frameworks, the financial
    services industry is well-equipped to manage new technologies
    and should be a model for other sectors. For decades, a robust
    technology-neutral regulatory framework has governed the
    adoption of emerging technologies.
        For example, consumer protection laws bar discrimination in
    lending, whether the decision is made by a human or by an
    algorithm. In our capital markets, rules against fraud and
    manipulation, along with circuit breaker technologies, help to
    mitigate risks related to potential AI-driven trading activity
    and our financial regulators have long provided robust risk
    management guidance, principles, and frameworks that address
    model IT third-party and enterprise risks.
        The overarching framework to ensure safe and responsible
    adoption of AI is in place. The challenge now is thoughtful
    application through sound, informed, and consistent regulation.
        To ensure the U.S. maintains its leadership and
    competitiveness, I accordingly offer five recommendations:
        First, regulators must support, not block, responsible AI
    adoption. We often hear that innovation is blocked by
    regulators at the examination and supervisory levels due to
    vague expectations and endless inquiries that lack clear paths
    to compliance. Examiners should be well-versed in the benefits
    and risks of AI and provide the marketplace with clear
    expectations.
        Second, we must ensure financial regulators have the in-
    house expertise and tools to keep pace with technology. A
    recent Government Accountability Office (GAO) report found a
    significant lack of technology skills at Federal financial
    regulators, which is why codifying innovation offices and
    equipping regulators with their own AI expertise, tools, and
    capabilities are essential for effective oversight.
        Third, Congress should establish a Federal data privacy
    framework and ensure open access to quality permissioned
    financial data. The quality of AI model outputs is inherently
    tied to the quality of data inputs. Congress should work to
    establish a national framework that governs data privacy and
    ensures that consumers have control over how their data is
    being shared and used.
        Fourth, Congress should prevent State laws from undermining
    Federal financial regulatory frameworks. Given the national
    nature of AI development and its application in the well-
    regulated financial services industry, a patchwork of State
    laws can create conflict, ambiguity, and confusion.
        Finally, regulators should further clarify risk management
    frameworks and promote the value of well-crafted standards.
    This includes making clear that the mere use of AI, even GenAI
    or agentic AI, does not inherently make an activity higher
    risk. Regulators can further help their efforts to keep pace
    with technological change by supporting well-crafted industry
    standards through regulatory recognition and safe harbors.
        Thank you, and I am happy to answer any questions you may
    have.

        [The prepared statement of Mr. Gorfine follows:]
        [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]

        Chairman Steil. Thank you very much.
        We now recognize Nicol Turner Lee for 5 minutes.

      STATEMENT OF NICOL TURNER LEE, SENIOR FELLOW AND DIRECTOR,
        CENTER FOR TECHNOLOGY INNOVATION, BROOKINGS INSTITUTION

        Ms. Turner Lee. Thank you, Chairman Steil, Ranking Member
    Lynch, and distinguished members of the subcommittee. Thank you
    for this invitation to testify on the use of AI in the
    financial sector.
        My name is Dr. Nicol Turner Lee. I am a director of the
    Center for Technology Innovation at the Brookings Institution,
    and I am also the co-author of a report that was done on AI in
    the global markets by the CFTC.
        Artificial intelligence brings a variety of opportunities
    to the financial sector, and for years it has been used in
    banking, fraud detection, mortgage applications, credit
    underwriting, and data analytics. Many of these use cases are
    promising, offering the potential for greater accessibility and
    improved customer service, while others may introduce
    challenges, including concerns about racial bias and
    discrimination.
        Given that the financial services industry is one of the
    country's most highly regulated ones, adoption and use of AI
    requires careful review. Without such oversight, risks abound
    that can undermine consumers' ability to be economically
    resilient, especially under changing economic conditions.
    Further, inaccurate or discriminatory information that is used
    to train AI models can put marginalized populations at even
    greater risk, threatening to widen the racial wealth gap, limit
    access to home ownership, credit, and other financial
    transactions. In other words, when algorithms make poor
    decisions, the quality of life for Black and Brown communities,
    seniors, and even some of us are placed in reverse.
        For these and other reasons, Congress must continue to
    foster responsible and ethical use in the financial regulation
    sector by providing safeguards that protect consumers from AI
    risks now and reinforce algorithmic accountability, safety, and
    security, especially when we look at incidents of fraud.
        Congress can also safeguard consumers from both the
    unintended and intended consequences through legislation, which
    starts with comprehensive national privacy standards and
    reinforcing the jurisdiction of independent Federal agencies
    and State attorneys general, who enforce consumer protection
    regulations in the age of AI.
        For example, the former director of the Consumer Financial
    Protection Bureau clarified that algorithmic decisionmaking is
    held to the same standards as human decisionmaking. The
    Department of Justice, the Department of Housing and Urban
    Development, these agencies also previously released documents
    stating the compliance with many of the provisions that apply
    to either tenant screening or fairness overall.
        But recent actions taken to defund and compromise the
    independence of Federal agencies like the CFPB, like the
    Federal Trade Commission (FTC), which have oversight over
    deceptive consumer practices, can have major implications on
    protecting consumers from the opaque technology of AI.
        State and State attorneys general are critical in enforcing
    those protections, but challenges to their State rights and
    laws are not productive. Even with the rejection of a 10-year
    moratorium on States' ability to develop their own laws, the
    current AI Action Plan still suggests that AI-related funds
    should not go to those with burdensome AI regulations.
        In the absence of a national framework, States must
    continue to protect the millions of seniors, children,
    marginalized populations, and even farmers who are being
    barraged by algorithmic discrimination, deepfakes, and other
    malicious attacks on their financial well-being.
        Recent proposals from legislators seek to establish
    regulatory sandboxes for AI developers where companies can
    apply for waivers or modifications but without strong
    regulatory or enforcement consequences, companies will have
    another avenue to exploit the personal information and
    behaviors of consumers.
        As my Brookings colleague Aaron Klein suggests, we should
    be creating more greenhouses, which allow for more transparency
    and sunlight into these processes, promoting collaborative
    partnerships between business, government, and consumers, to
    see where issues arise and how the three of them can foster
    trust with one another. These approaches can also assist in
    anti-bias discrimination, which is also very much imperative to
    building trust.
        I would like to just close with five proposals for members
    of this committee to consider as we embark on the inquiry
    today.
        Ensure that the industry is compliant with existing legal
    statutes and remedies which reinforce algorithmic
    accountability. The financial sector is already regulated by
    numerous Federal and State organizations. It is imperative that
    it stay that way, and that they also comply with acts like the
    Fair Credit Reporting Act and others to seek the prevention of
    discriminatory results.
        Mandate transparency guidelines and full disclosure for all
    consumers. Companies need to tell people publicly when they are
    using AI to make decisions. Those matter outputs should be
    explainable and accurate and reliable.
        Encourage, not discourage, responsible and ethical
    development of financial models. Innovation and regulation can
    be complementary, and I think that is to the benefit, again, of
    those greenhouses, which show transparency in the ideas and
    processes that we embrace in this area.
        Brace for the adoption of agentic AI but do the first two.
    Make sure there is consumer protection in place that we are
    poised to benefit from the autonomous nature of agentic AI, but
    we still have firm oversight.
        Finally I would say, invest in AI financial literacy
    programs so that consumers know how this industry and sector is
    evolving.
        These and other questions I place before this committee,
    and I look forward to your questions and collaboration.

        [The prepared statement of Ms. Turner Lee follows:]
       [GRAPHICS NOT AVAILABLE IN TIFF FORMAT]

        Chairman Steil. Thank you very much, Dr. Turner Lee.
        We will now turn to member questions. I will recognize
    myself for 5 minutes for questioning.
        I want to start with you, Dr. Cox, if I can. Let us do a
    real quick stage setting. Algorithms have been utilized in the
    financial services space since the 1980s, but we have now seen
    an explosive investment into AI. What is sparking this massive
    investment, in a short answer?
        Mr. Cox. Yes. I think what is sparking this massive
    excitement and investment is that this technology is general
    purpose in a way that previous technologies have not been. So
    there is a lot of work to train a system for, and now the same
    system can be used in many different settings.
        Chairman Steil. And it can pull through massive datasets in
    a way that was not possible in the 1980s.
        Mr. Cox. Absolutely.
        Chairman Steil. Although in the 1980s as well as today,
    there is already a regulatory framework in place, fair?
        Mr. Cox. Yes.
        Chairman Steil. Okay. Let me continue this. I want to come
    over to you, Mr. Gorfine, if I can. We are faced with something
    new as we focus on kind of risks and threats. The human mind is
    really good at thinking about what the risks are. It is not as
    good at thinking about the potential upside.
        If we think about what the Biden Administration did, they
    really took an attitude to the extreme in its handling of
    artificial intelligence. Fortunately, President Trump has
    reversed course on this, issuing Executive Order 14179,
    Removing Barriers to American Leadership in AI, and releasing
    America's AI Action Plan in July 2025.
        So this approach can really accelerate AI development
    through a try-first approach that removes some of the red tape
    and onerous regulations.
        I want to come to you, Mr. Gorfine. In your opinion, how
    can the AI Action Plan--or how is the AI Action Plan an
    improvement over the previous administration's AI approach?
        Mr. Gorfine. Thank you. Thank you for the question.
        I think that what we are seeing right now is an effort to
    look where there is existing regulation in place and taking an
    approach of letting us allow this to develop so we can actually
    identify risks and determine whether anything more is needed.
        I think, as many of us have outlined, the financial
    services industry is a heavily regulated industry. We have
    existing laws, regulations, and guidance that have been able to
    adapt and incorporate emerging technologies for decades. There
    are some approaches, including around the world, that are
    looking to do things kind of preemptively and in a more
    prescriptive fashion verse an approach where we say, hey, we
    have this scaffolding in place, we have principles in place to
    guide adoption of technologies in a responsible way, let us
    allow this to actually grow and develop.
        I think it is critically important that the U.S. does
    remain the global leader in AI, including in the financial
    services context. So the environment seems set to unleash that.
        Chairman Steil. Thank you very much.
        Mr. Reisman, I want to build on what Mr. Gorfine just said.
    In particular, let us dive into data privacy laws, something
    that is heavily regulated in the financial services space, but
    with the advent of AI, there are additional concerns that are
    coming online.
        So as financial services firms are increasingly deploying
    AI in areas like lending, fraud detection, customer services,
    what role will Americans' personal financial data play, and how
    do we use AI to implicate existing data privacy laws? Of
    course, the logical follow up would be, what should we be
    looking at from an AI perspective as it relates to data
    privacy?
        Mr. Reisman. Thank you very much for this important
    question.
        It has been heartening to me to hear how much we have
    talked about data privacy today, because I think what we would
    say is that a sound data privacy framework is a very important
    foundation for the development of AI and for America's AI
    leadership. I think data is the raw material on which
    artificial intelligence depends, and AI model developers depend
    on having rich datasets to develop high-quality models.
        One thing I think that often gets put into the discussion
    is the idea of a binary between privacy or innovation, and we
    would absolutely say it is privacy and innovation, and privacy
    is an enabler. So a good data privacy law with modern
    interpretation holds onto classic principles we have had for
    decades in privacy law but allows for flexible use of data for
    purposes such as model training and development.
        Chairman Steil. So building on what you are saying, do you
    believe that we can regulate and address AI under the current
    regulatory framework or do you need a new regulatory framework,
    which has been proposed by some?
        Mr. Reisman. What I would say is that, as a first step, we
    should look at the framework that we have, look at it closely--
    and a lot of the elements, as has already been discussed today,
    are there--and try and be very precise about identifying any
    lacuna but first, let us look at what we have.
        Chairman Steil. Dr. Cox, you are nodding. You agree that we
    can in many ways regulate AI through the existing framework?
        Mr. Cox. I think, certainly, the starting place should be
    what are the risks, what are the outcomes that you are trying
    to govern and control, and then any modifications that come for
    the technology can come from there but starting fresh.
        Chairman Steil. Thank you very much. I agree.
        I yield back.
        I will now recognize the gentleman from Massachusetts, Mr.
    Lynch, who is also the ranking member of this subcommittee. You
    are now recognized for 5 minutes.
        Mr. Lynch. Thank you, Mr. Chairman.
        This morning, a number of us had a meeting with Jack Clark,
    the co-founder and head of Policy at Anthropic and it was, I
    think, enlightening to hear him say how, for Anthropic, that
    the investment, the advancements, the deployment of AI had
    exceeded by about 5 years their expectations of the development
    and advance of AI, even based on the projections that they made
    3 years ago. They said, we are 5 years beyond where we thought
    we would be today.
        I just point to the velocity of change here in this sector
    and what a problem it creates up here, in terms of trying to
    regulate that and protect markets as well as protecting
    consumers.
        I know a couple of you have mentioned the sandbox model.
    Ms. Turner Lee, the Singapore model--and we actually had a
    congressional delegation from this committee go to Singapore
    and kind of review the model that they had for their sandbox
    back when they were doing fintech, a fintech sandbox and a
    crypto sandbox.
        So, interestingly enough, their sandbox has an ethical
    framework which begins with a principles-based approach so that
    there are elements of fairness and accountability, inclusivity,
    and it sets an ethical foundation for all AI projects. It has a
    bias mitigation element to their sandbox and mitigates bias in
    those AI systems, and developers are encouraged to assess their
    algorithms for potential discrimination.
        There are data transparency practices that must be complied
    with. Organizations must be clear about how data is collected,
    processed, and shared--explainability requirements within their
    sandbox model. So they must ensure that their AI systems can
    provide understandable justifications for their decisionmaking.
        There is also very strong stakeholder engagement,
    encouraging a wide range of stakeholders, and massive user
    privacy protections that are very important not only for
    Singapore but globally and certainly for the United States as
    well.
        There are regular reviews and audits of that sandbox
    process--sandbox process among these participants in the
    sandbox, as well as blueprints and guidelines and knowledge
    sharing.
        Is that the formula that we should use if we are going to
    go--I know the full committee chairman has an idea about
    sandboxes and we are going back and forth, he and I, about,
    what that should look like.
        I would just like to get your feedback. You have touched on
    a lot of the sensitive issues, and I would like to hear you
    extrapolate a little bit more.
        Ms. Turner Lee. Thank you so much for that question.
        I mean, I am a fan of sandboxes. I wrote a paper in 2018,
    when we were just beginning this bubble, on what algorithms
    were going to do when it came to consumers and regulatory
    sandboxes are a great way to experiment. In fact, here in the
    United States, we have used sandboxes to cultivate the fintech
    marketplace but because of the velocity of speed in which AI is
    gathering our personal information, the velocity in which we
    are going into transformational models where we cannot
    determine the beginning and the end. When we actually deploy a
    sandbox model in this day and age, it is important to have many
    of those verbals that the Singapore Government has put into
    place.
        It allows for accountability, continuous monitoring,
    transparency, and it also allows us to ensure that consumers
    are baked into the process, as opposed to providing waivers and
    exceptions to companies to experiment and then come back and
    tell us how it is all going to work, which is the current model
    from which we exercise right now when it comes to AI.
        Mr. Lynch. Thank you. One problem that I noticed in the
    Singapore model was the number of participants was limited, so
    it never scaled up. Some of the problems that we see with AI is
    when you get that scaleability.
        Is there a way for us to--I know the auditing and review
    after the sandbox is ongoing, but is there any way we can sort
    of make sure that scaleability effect is implemented inside of
    a sandbox?
        Ms. Turner Lee. Well, I think one of the areas in which
    many of us who have fought for consumer protections are
    interested in----
        Chairman Steil. The gentleman's time is--I will ask you to
    write that for the record----
        Ms. Turner Lee. No problem.
        Chairman Steil [continuing]. cognizant of the time.
        The gentleman yields, and the gentleman from Michigan, Mr.
    Huizenga, who is also the vice chair of the full committee, is
    recognized for 5 minutes.
        Mr. Huizenga. Thank you, Chairman Steil.
        Dr. Lau, we are talking about sandboxes. Do you have
    anything? I know you have been commenting on this in the past.
    Do you have any additional thoughts that you would like to----
        Mr. Lau. Definitely. We are actually participants in the
    Singapore AI sandbox that they have set up, the AI Verify
    Foundation. It has actually been working exceedingly well, in
    the sense that these types of government agencies need to bring
    the latest technologies to actually evaluate the latest risks
    that are emerging with AI agents and new AI technologies.
        So by bringing in innovative technologies to do that type
    of red teaming and testing, they are able to stay ahead. Think
    about here in the U.S., where we have regulatory agencies that
    are still being educated about the technology, whereas in
    Singapore they are able to unleash a lot more in terms of AI
    potential through bringing in new technology to test and
    evaluate it. I think there are a lot of benefits there and also
    to the point that Congressman Lynch mentioned around
    scaleability, red teaming is a very labor-intensive problem,
    right. So, you have thousands of tests that enterprises have to
    perform. If the Federal agencies can provide more guidance and
    tooling around that, it is going to accelerate AI adoption.
        Mr. Huizenga. Okay. That is helpful. Thank you.
        Staying with you here, we often hear of an AI arms race
    with China that, if lost, would threaten U.S. national security
    and the United States global economic dominance.
        In your opinion, is the United States currently--how are we
    currently faring, I guess, in this arms race? Are we failing at
    it? If so, how? More importantly, how can Congress and the U.S.
    Government writ large ensure that the United States outpaces
    China in the AI space?
        Mr. Lau. I think there have been different paradigms
    emerging here. One is where you have a Federal Government that
    can choose and pick and invest in winners of the space versus
    creating an open marketplace where different, say, large
    language model providers or vendors can compete within this
    marketplace, right.
        I think what we are seeing when we talk to folks within the
    Department of Defense (DOD), or Department of War now, we see
    actually a movement toward opening up these marketplaces where
    many different vendors can compete and the best solution rises
    to the top.
        Again, I will move back. We are actually evaluating which
    are the best solutions for use cases that is going to drive
    America's national security interests. It really comes down to
    can we do the right evaluation of those large language model
    providers or vendors.
        So combining open marketplaces that foster innovation allow
    different folks to compete, and then we get to choose the best
    ones for our use cases. It is absolutely essential here for us
    to be competitive in this arms race you are talking about.
        Mr. Huizenga. By the way, I deeply appreciate how you are
    succinctly wrapping up some very complicated and detailed
    things and allowing me 2 minutes yet to ask other questions.
    That is important up here when we only get 5 minutes.
        Mr. Gorfine, I want to talk a little bit about the impact
    of U.S. regulation and how it may threaten the use of AI in the
    financial services space. We had the Securities and Exchange
    Commission's (SEC's) predictive data analytics proposal under
    the Gensler SEC, which I believe would have had some
    significant impact.
        Are there other things like that we need to make sure that
    we are aware of and avoiding?
        Mr. Gorfine. Yes. I appreciate you raising the predictive
    data analytics rule. I mean, that was rescinded and I think
    that was an example of a non----
        Mr. Huizenga. Rightly so. Rightly so. As we saw with that
    SEC chair, he was expansive in his pursuit of covering every
    territory he possibly could.
        Mr. Gorfine. Right. I would agree it was not technology-
    neutral, and that should be kind of a guiding principle. It had
    incredibly broad----
        Mr. Huizenga. So it should be tech-neutral.
        Mr. Gorfine. Absolutely should be technology-neutral and
    importantly, like the soft power too and the way that
    regulators communicate to the marketplace is important. So what
    the----
        Mr. Huizenga. Oh, in other words, you do not want to be
    pounded into the ground and threatened with being put out of
    business? That does not foster innovation?
        Mr. Gorfine. That can be a challenge.
        Mr. Huizenga. Allow the sarcasm to be mine.
        Mr. Gorfine. That can certainly be a challenging
    environment, I think, especially when you are talking about
    smaller firms. When I think about regulatory messaging, it is
    the small firms, it is the community banks that do not have
    large armies of compliance teams that are able to parse certain
    types of messaging that comes from regulators. So it can serve
    as a big deterrent to adoption.
        Mr. Huizenga. In the last 30 seconds, how can these smaller
    community financial--you mentioned community banks or credit
    unions. How can they use AI to compete?
        Mr. Gorfine. Well, so I am a big believer--and I say this
    in my written testimony--in standard setting organizations.
    Like, industry-driven standards that regulators can effectively
    recognize or even create safe hardware would allow small firms
    to know that, if you are compliant with these best practices or
    standards, that is, from a diligence perspective, a route you
    can pursue. I think that exploring those types of models in the
    U.S. is going to be critically important as--I am out of time.
        Mr. Huizenga. I yield back. Thank you.
        Chairman Steil. The gentleman yields back.
        The gentleman from Illinois, Dr. Foster, the ranking member
    of the Financial Institutions Subcommittee, is recognized for 5
    minutes.
        Mr. Foster. Thank you, Mr. Chairman, and to our witnesses.
        I guess Mr. Reisman and maybe others have mentioned the
    importance of privacy enhancement techniques. Several years ago
    when I was chairing the AI Task Force on this committee, we had
    dragged in a witness to talk about homomorphic encryption and
    some of the differential privacy techniques and I noticed just
    recently--and it was clear back then that these were not ready
    for prime time. There was a huge penalty for performance, and
    the privacy was not actually that great.
        I noticed recently Google just announced this Google Vault
    Gemini, which sounds like they are actually implementing
    training with differential privacy.
        So I was wondering if anyone on the committee could say
    something about are these techniques really ready for prime
    time, and is there the possibility of a regulatory safe harbor
    for firms that commit to using high-quality differential
    privacy type tools?
        Mr. Reisman. Thank you for the question.
        It has been quite extraordinary to see the progress on
    privacy enhancing technologies over just the last few years. I
    think you are right that not so long ago many of them were not
    ready for prime time, but the curve has been quite steep in
    terms of many of them becoming much more feasible.
        Part of it is that many privacy enhancing technologies are
    compute-intensive, but our computing power collectively is
    growing a lot stronger, so we are able to handle that. I think
    there was also a sense that a lot of them were complex and
    maybe out of reach, especially for smaller companies.
        There is a whole ecosystem now of expert companies that are
    able to consult and offer what you might call off-the-shelf
    privacy enhancing technologies, to make them much more
    available much more broadly. So----
        Mr. Foster. It would make sandboxes easier to implement.
        Any other sort of comments on the state-of-the-art? Would
    anyone disagree that this is something that is pretty promising
    at this point?
        Mr. Cox. Things like fully homomorphic encryption provide
    very strong guarantees and you are correct that they are slower
    than conventional computing, but the leaps and bounds that they
    are making in that technology, it is orders of magnitude,
    thousand times better. Every time I turn around, it has gotten
    better.
        So I think that is moving very fast, and the harbor is
    going to ultimately start to close that gap as well. So I think
    we are going to benefit from that wave.
        Mr. Foster. That could be a key component to a safe sandbox
    in a lot of these things.
        Mr. Cox. Absolutely.
        Mr. Foster. Relating to one of the things we struggle with
    is the small bank versus large bank, trying to level the
    playing field if we can and it is one of the things that AI can
    potentially help us with.
        Soon all of us will have in our pocket the best team of
    lawyers that has ever been assembled. So it means if you ever
    get in a fight with a billionaire, a legal fight, the
    billionaire will not be able to get a better legal team than
    you have essentially for free.
        Now, similarly, a small bank should, through AI, be able to
    have access to a really good AI risk adviser, a team of risk
    adviser AIs, so that even if it is a pretty small bank, this
    team will know every way that any bank has failed in the
    history of this country and just have a tremendous amount of
    knowledge and make it easier to operate a small bank.
        Similarly, regulations, regulatory technology (RegTech), I
    think, is another real advantage. I did an interesting
    experiment a couple weeks ago where I said, okay, Claude, or
    whoever I was using, give me the balance sheets for three banks
    that have just failed for typical reasons, and it just knocked
    it out of the park.
        Then I said, now give us a resolution plan for each of
    those three and it was great. I mean, it just said, okay, in
    this case set up a bridge bank. In this case, try to merge it.
    In this case, just liquidate it. They were very sophisticated
    things, and I was impressed.
        I was wondering if any of you have a feeling for whether
    that is really kind of the future of regulation, that even
    small banks have all of their records electronic.
        If there was a standard interface for the accounting
    software that gets run that could report up to risk management
    and to the regulators, you could have real-time stress testing
    against dozens of scenarios every single night for the smallest
    bank, and that would be a real leveler.
        Any thoughts? Is there a reason why things cannot evolve in
    that direction?
        Dr. Lau.
        Mr. Lau. Thank you. Yes, certainly. We work with a lot of
    community banks and regional banks, backed by a number of them
    and we see this every day, right. So this new AI technology can
    empower them to automate or streamline a lot of these
    compliance work flows that are very manually intensive that
    they just do not have staffing to do, but also, as you
    mentioned, unlock really new types of auditing and risk
    controls, so 24-hour continuous monitoring and observability
    into certain types of work flows that are highly regulated.
    That is all really exciting and actually being realized/
    materialized today across a lot of these smaller banks but I
    would say on the flip side, you also want to make sure that as
    these AI technologies enter into these regulated work flows,
    they are also having the right guardrails in place and they are
    being used for those work flows appropriately and aligning them
    with proper bank policies and procedures. So that is something
    that actually requires technical expertise.
        Chairman Steil. The gentleman's time is expired. We ask you
    to complete that in writing.
        The gentleman from Ohio, Mr. Davidson, who is also the
    chair of the Subcommittee on National Security, Illicit
    Finance, and International Financial Institutions, is
    recognized for 5 minutes.
        Mr. Davidson. Thank you, Chairman.
        Clearly, AI represents a transformative force, one that can
    exponentially enhance productivity, bolster our financial
    system's global edge, and it will no doubt influence our
    culture. Technology never exists in a vacuum, and I have long
    warned about the erosion of our Fourth Amendment protections in
    the digital age.
        The Uniting and Strengthening America by Providing
    Appropriate Tools Required to Intercept and Obstruct Terrorism
    (PATRIOT) Act, for example, massively expanded domestic
    surveillance. The Bank Secrecy Act had long ago obliterated any
    real claim to privacy in your financial dealings. Most State
    bureaus of motor vehicles are monetizing the personal data
    citizens are required to provide just to drive or obtain ID.
    Now, the government is outright buying data that would
    otherwise require a warrant or a subpoena, sidestepping the
    Fourth Amendment entirely.
        Because we are serious about fostering innovation, and we
    are serious about our Constitution, we also need to recognize
    that AI should serve the American people without turning it
    into another tool for unchecked surveillance or data
    exploitation.
        Mr. Chairman, I would like to submit this document for the
    record. In my recent op-ed for the Daily Caller, I emphasize
    privacy is the foundational layer for ethical AI.
        Chairman Steil. Without objection.

        [The information referred to can be found in the appendix
    on page 96.]

        Mr. Davidson. Thank you, Chairman.
        Without it, we are handing the keys over to a surveillance
    state on steroids. I mean, we need to update the regulatory
    framework for AI or build it in Congress, and we certainly need
    to address privacy, because that is the dataset that AI is
    using. Urgent questions, including who is liable if AI is
    misused? Who profits and how when someone else's data or
    intellectual property is indexed or shared with AI? When does
    law enforcement need a warrant, if ever?
        These are not abstract questions. They are urgent, and
    Congress needs to be proactive, not reactive, to set clear
    boundaries. We cannot let AI become another excuse for Big
    Brother to pry into our lives.
        So what safeguards do we need? We should learn from other
    jurisdictions, but we should, of course, chart our own course.
    The EU's AI Act, for instance, takes an approach with outright
    bans on real-time biometric surveillance and social scoring,
    measures that align with protecting individual liberties from
    invasive tech but in other ways, the European Union has become
    pretty Orwellian with some of their privacy approaches and
    speech limitations.
        So positively framing safeguards around AI is important.
    Freedom surrendered is rarely reclaimed, so I am encouraged by
    the Trump Administration's AI Action Plan with its emphasis on
    accelerating innovation, building infrastructure, and leading
    globally. It promotes open source development, cuts red tape,
    and streamlines permitting without smothering the private
    sector.
        Mr. Reisman, how are financial regulators protecting the
    vast amounts of sensitive financial data that the government
    already collects from AI exploitation?
        Mr. Reisman. Thank you for the question.
        First of all, I just want to--to your point about raising
    data privacy and this very important foundation for a sound
    environment, not only for keeping people safe but for
    innovation. I would underscore our sense that having a sound
    privacy framework in the United States is actually not only
    consistent with but important for achieving the goals of the AI
    Action Plan. It is encouraging to see progress in that area.
        I think others may have more experience than me with what
    is going on inside the agencies for what they are doing to keep
    data safe, so I would defer to others.
        Mr. Davidson. Anyone else?
        Mr. Gorfine. I am happy to take that. I would agree that
    data is the one place screaming for Federal legislation to
    create a proper baseline for how we secure data that will be
    consumed by AI models.
        With respect to your question, what can regulators do? We
    were just talking about privacy enhancing technologies, things
    like encrypting data. Ultimately, financial regulators need to
    be recognizing that there are these developing privacy
    enhancing tools and making sure that regulated financial
    institutions can use those technologies.
        So data can be encrypted. We can avoid creating honey pots
    of information that are being sent every which way. I mean, the
    reality is today all of our information--driver's license
    photos have been emailed, sent to every single provider. There
    is a better way to move encrypted information and limit access
    to who actually gets it and when.
        Mr. Davidson. Thank you. I think the computing architecture
    is really important. I have always liked blockchain
    technologies. I like zero knowledge proofs and ways to protect
    data that way. The government in some cases has artificially
    limited that.
        I will submit written questions for the record. I am really
    curious how you protect intellectual property, copyrights,
    trademarks, and maybe monetize that for other people down the
    way. Maybe blockchain does it. Maybe there are other ways but
    thank you for your expertise and attention.
        Thanks for this hearing, Chairman, and I yield back.
        Chairman Steil. The gentleman yields back.
        The gentlewoman from California, the ranking member of the
    full committee, Ms. Waters, is now recognized for 5 minutes.
        Ms. Waters. Well, thank you very much.
        Dr. Turner Lee, we have seen development of new AI
    technologies like agentic AI, which can operate independently
    and autonomously. Agentic AI is capable of self-directed and
    complex behavior and can also carry out multi-step tasks.
        Financial services companies are now experiencing and
    experimenting with agentic AI in investments, credit
    decisioning, and more. However, I am concerned about the new
    scale of risk and vulnerabilities from all of this.
        Dr. Turner, can you elaborate on the potential risks for
    agentic AI and whether a liability framework can help us
    mitigate those risks? Who is responsible for the actions of an
    AI agent if something goes wrong?
        Ms. Turner Lee. Thank you so much, Congresswoman, for that
    question.
        I think most of the conversation we have had today is on
    this promise of AI as it relates to efficiency in the financial
    sector and agentic AI as an extension of that. The more and
    more financial institutions can become more autonomous through
    chatbots and other tools that allow people to not necessarily
    talk to a financial counselor but to talk to AI to be able to
    get through their steps. It makes sense on the productivity
    side, but it does not make sense for the consumer.
        The consumer has a lot of reputational risk that comes with
    this, a lot of financial risk and in an industry where we know
    that consumers are at the heart of any type of harm or
    potential harms that come through misadvice or miscalculation,
    it could have a huge effect not only on people who are wealthy
    but people who are experiencing the wealth gap.
        So I appreciate your question. I do think we need a
    liability risk structure for this, one in which Congress thinks
    through the same type of consumer protections that we are
    defunding right now and diminishing, even with many of the
    actions today to append those regulatory agencies like the FTC
    and the CFPB.
        As we move into agentic AI, without those guardrails and
    the ability of States, in particular, to control how people
    respond to this new technology, it is actually going to go
    further than we can catch up with it.
        Ms. Waters. Wow.
        Dr. Turner Lee, the Republican regulatory sandbox proposal
    for AI we are considering today appears to be just more
    deregulation framed as innovation and lacks requirements for
    public disclosure, harm mitigation, and other important
    protections, with an unlimited scope and virtually no
    limitation, putting Americans, consumers, and other market
    participants at risk.
        I am deeply concerned that regulatory sandboxes may remove
    safeguards from a rapidly developing AI market that is already
    lacking meaningful Federal regulations and oversight. In fact,
    we have yet to fully understand the consequences of deploying
    this technology without any safeguards and are already dealing
    with countless public safety issues.
        Despite the fact that you have alluded to some of this,
    what risks do regulatory sandboxes pose? If Congress were to
    consider regulatory sandboxes for AI, what kind of standards
    should responsible sandboxes meet?
        Ms. Turner Lee. Thank you for that question as well.
        I mean, I think we have mentioned already the role of
    sandboxes in helping us to cultivate new products and services
    within the sector--and among other sectors. We actually see
    sandboxes in healthcare.
        The challenge is, without the right variables that we are
    actually constructing to make sure they are safe, ethical,
    fair, inclusive, as has been mentioned, sandboxes will turn out
    to be an exceptional exploitation of consumers.
        What that means is, without any guardrails--which, the AI
    Action Plan is suggesting there should be modifications, there
    should be waivers. We really need sandboxes to be very
    transparent. We need clear goals and questions about what it is
    trying to solve. We need protections against consumers who are
    part of those sandboxes to ensure that any harm that they face
    is--there is retribution for that as well.
        In my opinion, we can actually create this without putting
    people at risk in terms of the end product, and we can do it in
    the light, as opposed to in the dark, when it comes to
    companies and government working together on those policy
    concerns.
        Ms. Waters. I am so pleased that you are here today.
        Ms. Turner Lee. Oh, thank you and I am pleased you are here
    today too.
        Ms. Waters. I am going to call on you to continue the kind
    of education that is needed with Members not only of this
    committee but this entire Congress. I am particularly concerned
    about the possibility for wrongdoing, the possibility for
    discrimination----
        Ms. Turner Lee. Yes.
        Ms. Waters [continuing]. all of that and I want to learn
    more about the databases that are being used and how they are
    significant in determining the outcomes.
        Ms. Turner Lee. Yes.
        Ms. Waters. Thank you so very much.
        Ms. Turner Lee. Thank you.
        Chairman Steil. The gentlewoman yields back.
        The gentleman from Tennessee, Mr. Rose, is recognized for 5
    minutes.
        Mr. Rose. Thank you, Chairman Steil, and thanks to Ranking
    Member Lynch for holding this important hearing.
        Thank you to all of our witnesses for taking time to be
    with us today.
        I want to start with you, Dr. Cox. Could you please
    describe the concept of technological singularity and share
    your perspective on whether you believe AI will achieve
    singularity and provide an estimate on the possible timeframe
    for this development?
        Mr. Cox. Thank you for the question.
        So the idea of a singularity is, we reach a point where the
    technology is able to advance its own progress faster and
    faster and faster, such that it can get sort of a positive
    feedback loop and then we suddenly have an explosion of
    capability.
        One of the things that is interesting that--I just dusted
    off a copy of ``The Singularity is Near'' by Ray Kurzweil that
    happened to be on a shelf that I was cleaning up. One thing you
    will see about futurists is, they often get the shape of things
    right, but the years are difficult to predict. I would not
    hazard to make a guess about when that is going to happen or if
    it is going to happen but I would just say that I do not think
    we are anywhere near--as somebody who works with this
    technology day-in and day-out, I do not think our biggest risks
    come from, sort of, some eclipse of ``AI is better at
    everything than humans are,'' but certainly our labor-market
    issues that we have to deal with as sub-tasks of a job are
    displaced but I do not think we are in imminent danger of
    anything so extreme as a singularity.
        Mr. Rose. Thanks. I appreciate the insight.
        Dr. Lee, I found an interesting article from the nonprofit
    Cash Essentials that states, quote, Some AI-driven systems may
    treat cash transactions as suspicious, leading to increased
    scrutiny and regulatory pressure that discourages cash use,
    unquote.
        How can we ensure that AI does not discriminate against
    individuals who use cash? Additionally, what measures can be
    taken to prevent AI systems from falsely flagging cash
    transactions as suspicious, thereby avoiding undue pressure on
    companies and regulators to favor cashless payments over cash
    transactions?
        Ms. Turner Lee. I do appreciate that question because I
    think we are seeing, based on the regulatory sandbox that we
    used to create the fintech industry, a lot more transactions,
    especially among the unbanked or underbanked.
        To your point, here is why I think AI could be an
    interesting tool to help us combat fraud. Improved analysis
    through AI detection systems could actually be helpful there.
    Using AI in ways that the AI sort of cleaves with the data
    about the underbanked or unbanked and combines that with
    traditional legacy systems could also be helpful.
        We also talk about AI as just taking people's data, but it
    also considers other externalities, like where you live, what
    your ZIP Code is, these other proxies. Oftentimes those proxies
    are for the worse, in terms of bias and discrimination, but
    they could actually also be helpful in helping us understand
    what the new, modern economy looks like in banking.
        So I would suggest that there are some techniques on the
    technology side, but there is also room for people like me, as
    a sociologist, to come sit at the table and help determine how
    we do this better.
        Mr. Rose. We already see--without the benefit of AI, we see
    cash transactions being discriminated against in a number of
    ways. It is a very real concern to me.
        Mr. Reisman, with the increasing prevalence of artificial
    intelligence, it is clear that AI-assisted fraud will likely
    escalate rapidly, employing novel tactics to deceive consumers.
    In my view, it is essential to warn the public about emerging
    AI-driven fraud schemes as soon as they are identified.
        How can private companies and industry stakeholders
    collaborate proactively to anticipate and combat the evolving
    threat of AI-assisted fraud?
        Mr. Reisman. Thank you for the question.
        I share your concern about fraud, and I think I have heard
    a lot of folks say we are in a moment where the defense has to
    keep up with the offense, right? We want to make sure that our
    financial institutions are empowered to have the same tools to
    detect fraud that the fraudsters are using to advance it.
        That is the most important, is that our institutions,
    whether they are small banks, whether they are large ones, have
    access to that state-of-the-art screening technology.
        I think--one thing that I talked about in my opening
    testimony was the importance of dialog and I think making sure
    that we have spaces, whether it is through sandboxes or whether
    it is through other mechanisms that are set up by the Congress
    or by the regulatory agencies, to constantly be sharing
    information about these advances in technologies and about
    these fraud capabilities so that we are all collectively
    working together on solutions.
        Mr. Rose. Thank you very much. I appreciate those insights.
        I yield back.
        Chairman Steil. The gentleman yields back.
        The gentleman from California, Mr. Liccardo, is recognized
    for 5 minutes.
        Mr. Liccardo. Thank you, Mr. Chair.
        Thank you all for your testimony and for taking the time to
    help educate us, myself in particular. I know this is a fast-
    moving area and I really had questions targeting really
    primarily Dr. Cox and Dr. Lee. I would be interested in your
    views.
        We have several concepts that have been presented in
    legislation here before us, and I wanted to see if there was--I
    would like to tinker a little bit with a couple of these bills
    from Chairman Hill, the sandbox concept, and from my colleague
    Congresswoman Pettersen, the task force concept, and combine
    them in a way, if we could.
        I represent Silicon Valley. Obviously, we have a lot of
    concerns--or I hear lots of concerns in my neck of the woods
    about having government involved in regulating the code and the
    algorithms, particularly since government is not terribly good
    at it. We would expect that the technology is evolving so
    quickly that we will not be good at it but, on the other hand,
    I think there is a widespread embrace of the notion that we
    need to be mitigating the worst of the harms.
        So the question would be: If we were to create perhaps even
    more than a task force but an independent body of folks in
    industry, academics, experts, financial regulators, and others
    that were to establish what the best practices are in the
    industry--the best practices around, for example, curated data
    sets that I know you mentioned, Doctor, that IBM utilizes, or
    testing and reporting, or fraud detection, watermarking, data
    security--a whole host of best practices in the industry, and
    then hold that up as the standard, and establish, essentially,
    if that is going to be the negligence standard--or,
    essentially, the standard, if everyone complies with that
    standard, then you are exempt from liability. If you are not
    meeting that best-practices standard, then good luck with the
    lawyers and the regulators.
        What about an approach that would combine those two?
        I would ask either Dr. Lee or Dr. Cox, if you would like to
    jump in.
        Ms. Turner Lee. I will jump in first.
        Mr. Liccardo. Yes.
        Ms. Turner Lee. I think that is a tremendous idea that we
    should consider, because we seem to be at the same stalemate
    every time we talk about these issues.
        There is a technical side of it, and then there is this
    consumer output, and I do think that government regulators are
    good at the latter, the consumer protection side of it,
    because, guess what, that is the side where our constituents
    are coming to us and saying, ``Something has harmed me.''
        With regards to what you are saying, I would just like to
    offer to you that we did start that process under the previous
    administration. The Blueprint for an AI Bill of Rights was
    actually a nice glide path for getting to the same protections
    that you are speaking of, as well as collaboration. The
    executive order, which was soon after appended, had a lot of
    that conversation on how do you actually bring different bodies
    together from various disciplines, industry sectors,
    government, and civil society so that we actually solve this
    together.
        To date, we have seen a lot that sort of eroded in the new
    administration as well as in the AI Action Plan, where we are
    primarily competing against ourselves, when we actually just
    see China as our only force of nature.
        So I would suggest--I agree with you. I think there has to
    be more conversation, more collaboration, more disclosure among
    the various entities to get to the space that you are talking
    about and I think a task force would not be a bad idea. That
    was also recommended under the prior administration.
        Mr. Liccardo. Thank you, Doctor.
        Dr. Cox?
        Mr. Cox. I think that there are some interesting things
    emerging already. The ISO 42001 standard for just the entire
    creation process of a large language model is one example of
    something emerging, and I think that gives you a little bit of
    a sense of how these things are playing out. It is a voluntary
    standard, you get audited against it, and it is some sort of
    mark of whether you have the proper hygiene there.
        Now, that is different than regulating the algorithm,
    though, right? Like, it is----
        Mr. Liccardo. Right.
        Mr. Cox [continuing]. more about regulating processes and
    controls----
        Mr. Liccardo. Right.
        Mr. Cox [continuing]. and documentation and auditing, as
    opposed to saying, this technology, this algorithm is
    intrinsically worrisome somehow.
        I think that is one thing that I think many of the
    panelists have raised, is that there is nothing intrinsic about
    the technology; it is, how are you using it? It always has to
    be in the context of how it is being used and we have use-and-
    risk-based regulation. That is a normal thing that we already
    have. So combining that with things like cybersecurity hygiene
    standards, which are now then being transported to the world of
    AI, I think that is--I think that is an evolution of what we
    already have to a good outcome.
        Mr. Liccardo. All right. Thank you. Thank you both.
        I yield.
        Chairman Steil. The gentleman yields back.
        The gentleman from Montana, Mr. Downing, is recognized for
    5 minutes.
        Mr. Downing. Thank you, Mr. Chair.
        Thank you to the witnesses.
        This is a really exciting topic for me. The thoughts of
    AI--I came out of technology--the opportunities are incredibly
    exciting, but it is also a little bit scary in where it goes,
    and it is interesting--I was talking to Eric Schmidt last year.
    He wrote a book on AI with the late Henry Kissinger, and he
    made a comment that I thought was really interesting, about
    being very light on how you regulate it so that we are not
    blown out of the water by our competition. Then he said
    something that really caught my attention: ``But you need to be
    ready to unplug it.'' I was not exactly sure what that meant,
    but it was an interesting comment that I am still dwelling on.
        As a former regulator, we had to deal with a lot of the
    issues with artificial intelligence as a tool for industry and
    really what the implications were. Some of the interesting
    things we did as an insurance regulator is--we were of the
    opinion that, once you wrote down a rule or a law or something
    prescriptive, it was probably already stale, because this is
    just evolving so quickly.
        So what we tried to do to inform industry on how we were
    looking at it is, all the work we did on that committee was--it
    was concept space, non-prescriptive, because you wanted to kind
    of guide how we as regulators were looking at it without
    saying, ``This is what you have to do.''
        A lot of the questions that came up there were is the
    machine biased? Is the machine bad? Is the machine--a lot of
    this kind of concern about what was coming in it.
        You know, me, you know, I think about the data. As a former
    researcher, I think garbage in, garbage out. What is the data
    you are training it with and what are the thought processes on
    how broad and how deep that data set is and what kind of
    results you have?
        But the results coming out of it--a lot of folks were
    saying, ``Well, at least in terms of a regulator, we need to
    have causation and not correlation.'' You know, I do not agree.
    I think if you have a high statistical probability of getting
    the same results from this system--a lot of our life is based
    on correlation and not causation--I think you can use that as
    reality or close to reality.
        Then, if it is giving you a result you do not like, like it
    is affecting a protected class, if it is something that you do
    not like, that is the public-policy decision, not ``machine
    bad.'' Then you have the real, honest conversation about that
    public policy.
        Another thing that folks would say on the regulator side
    very often is, ``Well, we need to be able to look into that
    black box.'' I think about, well, that black box--once you have
    an N-equals-close-to-infinity access neural network, no human
    can understand that. What tools do you need to understand that?
    You know--this is my opinion--the tool you need to understand
    that is probably generated through AI.
        So you have AI doing that, and it comes back to the ancient
    question of, ``Quis custodiet ipsos custodes?'' ``Who is
    watching the watchers?'' You know? It is an interesting problem
    to come up with.
        I am going to start--sorry about--thank you for indulging
    me on that.
        One of the things that I think about a lot is, as a State
    regulator, States' rights are very important to me and there
    has been a big conversation about that. I strongly favor
    allowing States to lead on regulation where feasible but at the
    same time, it is essential that this technology has the legal
    and regulatory flexibility to continue innovating and develop
    in the United States.
        So I am going to start with Mr. Reisman here.
        What role do you think the States should play in regulating
    AI or are you of the opinion that this is something that
    Congress needs to tackle?
        Mr. Reisman. Thank you for the question.
        I think the first question that we should be asking is, do
    we need more regulation on AI at all? We have had an
    interesting discussion about looking at what powers we already
    have, whether that is under Federal law or under State law,
    that allows us to address a lot of the questions that we may
    have about AI.
        I think that there is--one thing that we have seen is, for
    both consumers and for businesses, it can be hard when there is
    a kaleidoscope of different State regulations and so there is a
    value in having interoperable Federal standards. I think there
    is an interesting question to be explored further about whether
    there may be particular elements that States need to address as
    a----
        Mr. Downing. Right.
        Mr. Reisman [continuing]. complement but not substitute for
    that.
        Mr. Downing. Yes. Thank you on that.
        In the interest of time, I am going to move on.
        The Biden Administration seemed determined to stifle AI in
    any way it could, focusing more on potential threats than its
    clear benefits and I do believe the benefits are strong.
        So I am going to move to Mr. Gorfine, please.
        Could you describe some of the most concerning aspects of
    the Biden Administration's approach to AI, from executive
    orders to a so-called AI Bill of Rights, and discuss what the
    impact would have been had Congress passed what the Biden
    Administration proposed?
        Chairman Steil. The gentleman's time has expired, but we
    will ask the witness to provide that for the record.
        Chairman Steil. We thank you.
        The gentleman yields back.
        Mr. Downing. On that, I yield. Thank you, Chair.
        Chairman Steil. The gentlewoman from Massachusetts, Ms.
    Pressley, is recognized now for 5 minutes.
        Ms. Pressley. Thank you.
        Thank you to our witnesses for joining us today for what is
    really a timely hearing.
        When I am in my community in the Massachusetts Seventh,
    people are very animated about the future of artificial
    intelligence. I hear it all, ranging from fears of AI bias to
    excitement for innovative opportunities, to concerns about
    implications on the future of work, to confusion about what is
    next. I am proud that we are confronting these issues head-on
    today.
        September 26 to October 3 marks Boston AI Week. Startups,
    investors, researchers, and students will all convene to
    explore the role they play in the evolving AI landscape.
    Leaders in AI, like the Mass Technology Leadership Council,
    will host educational and networking events to build on State
    investments in the next generalization of leaders, educators,
    and workers in this rapidly growing field.
        I am proud to represent a district that is trailblazing the
    development of the AI industry. In Massachusetts, jobs in the
    technology sector make up 14 percent of our labor force,
    compared to 10 percent nationally and we know diversity in AI
    jobs is essential to confront bias, to maximize opportunity,
    and to make good decisions that help everyone benefit from
    these cutting-edge technologies.
        Dr. Turner Lee, I want to focus in on your research about
    the role diverse teams play in AI development and deployment.
    What are some ways diverse teams may be helpful to promote
    ethical and innovative uses of AI?
        Ms. Turner Lee. Thank you so much for that and I
    congratulate you on the AI Week in Boston.
        So I would just say there that it is important to have
    representative groups for a couple of reasons that we have
    spoken about today. I think, on both sides of this
    conversation, there is this tremendous excitement for the
    opportunities and then a tidbit of fear--and, depending on who
    you are, more or less, right?--based on what AI can do.
        It is important that we have people who have the lived
    experiences of the variety of impacts that AI can have. Why
    that is important, with any other internet technology that we
    have had, is because AI, particularly the financial sector, is
    dispersed to be very individual to that person.
        So, when we talk about data, data that is traumatized or
    discriminatory, that actually takes the account of the wealth
    gap that is experienced by, for example, Black populations,
    where they have been denied credit, loans, and other
    eligibility, shows up in the data. When you do not have people
    who understand that lived experience, that experience passes on
    generationally, and the AI tends to then affect their quality
    of life. Loan denials continue, et cetera.
        So I think it is really important to have not only
    background diversity but diversity of various people from
    various disciplines, various sectors, sitting at the table. How
    are we building financial-sector AI without people who actually
    understand how community banking works or sit in the roles of
    experienced financial literacy counselors, for example? You
    have to have everybody there.
        Ms. Pressley. Thank you.
        Dr. Turner Lee, how do you believe the Federal Government
    has a role to play in this and what is that, to ensure that
    people from all walks of life--women, people of color, low-
    income folks--can have careers in AI, especially in the
    financial services sector?
        Ms. Turner Lee. Well, I think you pointed it out really
    well when you talked about trying to empower local
    entrepreneurs. It is important for the Federal Government to
    create low benchmarks to entry. We see on this panel a young
    man who has his own company that is doing incredible things
    when it comes to data architecture, AI infrastructure, et
    cetera.
        Giving opportunities for a variety of people to participate
    in this ecosystem is really important, and I think the
    government could actually find ways to incentivize the private
    sector to be more inclusive of diverse founders and
    entrepreneurs and small businesses that want to participate in
    this space.
        I also think the Federal Government could do more on AI
    literacy, what does a national AI literacy initiative look
    like, so that people know that this is not about being an
    engineer; this is about actually experiencing this
    behaviorally. The way that AI is dispersing, this is not going
    to be just about experiencing it on your computer; it is going
    to actually show up in your refrigerator and other places.
    People need to know that.
        I would say the other thing that Congress should do is
    close the digital divide. We keep talking about AI, but we
    actually have not closed the basic infrastructure issue. We
    cannot build the compute facilities and data centers without
    that.
        Ms. Pressley. Thank you, Doctor, for being so prescriptive
    there.
        Whether it is a hearing in Congress or a meet-up at the
    Museum of Science, AI is the topic of conversation, and we have
    the responsibility to ensure that all voices are included,
    because AI works best when it works for all.
        I yield back.
        Mr. Downing [presiding]. The gentlewoman yields.
        The gentleman from Iowa, Mr. Nunn, is now recognized for 5
    minutes.
        Mr. Nunn. Well, thank you, Mr. Chair.
        I want to thank the panel for being here.
        Chair, as a fellow Air Force fellow, it is good to see you
    in that seat.
        As we look at what Congress has been trying to grapple
    with, this idea of artificial intelligence--I have only been
    here two terms, and we have talked about it every single year.
    In fact, we were on the AI Task Force, something this committee
    helped lead to be able to address real solutions but I think
    Washington does a lot of talking, and we should be doing a lot
    more listening, particularly to practitioners in the field who
    have been addressing this--combating it, addressing it, and
    finding new solutions for it--and incorporating your experience
    into the way that we do policy. As opposed to DC. being the one
    who writes the rules of the road and then expects you to
    execute them, we should be doing this in collaboration.
        Mr. Chair, it is one of the reasons I am leading a piece of
    policy called the Artificial Intelligence Practices, Logistics,
    Actions, and Necessities (AI PLAN) Act, or the Artificial
    Intelligence PLAN Act, to ensure that we here in Washington are
    incorporating and getting our own house in order before we
    start telling the innovators, the pioneers, and, candidly, the
    defenders how best to do it under a government auspice.
        I look back at where we were with cybersecurity when I was
    Director of Cybersecurity at the National Security Council
    (NSC). Many of the solutions were already being provided out of
    the public sector and the private sector when we worked
    together.
        With that, I want to be able to dive into a couple of
    issues that have been addressed when it comes to artificial
    intelligence.
        First and foremost, the idea of misinformation and how it
    happens not only in the financial space but across our
    government. It has lowered the barrier to entry. It has allowed
    misinformation to expand at an accelerated rate. We saw even
    this week, tragically, with the death of Charlie Kirk, Russian
    bots providing inception for everything from conspiracy
    theories to misaligning our own communications right here in
    our own country.
        Dr. Christian Lau, you have been a leader at Dynamo AI. You
    have made an effort to really bring this technology on board. I
    want to ask you, what should Congress be doing right now to do
    those things like combating state-sponsored terrorism,
    including information warfare?
        Mr. Lau. Yes. That is a great question.
        Particularly, you mentioned the PLAN Act, which you
    proposed, which I think is a really great step forward in
    looking at these different risks--how AI can be used for
    misinformation, but also used for different types of financial
    crimes, right?--and threaten systemic risk to our system.
        One thing that I would emphasize is that keeping up with
    the pace of the technology is key, and talking to practitioners
    about the latest risks is absolutely essential to making
    effective legislation, right?
        One thing that I had an opportunity to talk to your team
    about is the advent of AI agents and particularly, you brought
    up cybersecurity risks----
        Mr. Nunn. Right.
        Mr. Lau [continuing]. right?
        So AI agents is where you give these systems more autonomy
    to carry out end-to-end workflows and tasks. A lot of times,
    they can go in many different directions, but this also opens
    up new risks for agents to potentially exfiltrate data or for
    State actors to leverage agents and actually send what we call
    ``prompt injection'' or ``jailbreak'' attacks to actually
    exfiltrate information from protected systems in financial
    services or elsewhere.
        So an AI agent can pull an email, that email could have an
    embedded hidden instruction or prompt injection, and that agent
    can then be overridden to actually perform malicious tasks like
    crash a system or exfiltrate sensitive information.
        So I think the work that you have been doing on this act,
    as well as talking to leading practitioners, is absolutely key
    to keeping up with these risks.
        Mr. Nunn. I could not agree with you more that the
    challenge here is, the technology is going to move much faster
    than the policy is able to keep up. So being able to provide a
    framework versus prescription is something the AI PLAN Act
    intends to do but also brings in best practitioners in this.
        I want to talk with you, Matt Reisman. You are at the
    Center for Information Policy Leadership. You have seen some of
    these innovations come on board. Talk to us a little bit about
    how not only we incorporate them to have a better safeguard for
    our government and our citizenry but, where the innovation is
    really being pioneered a lot of times by our private-sector
    partners, how should we be incorporating that into how we work
    here in Washington, DC.
        Mr. Reisman. Thank you for the question.
        I think, number one, one thing that has been encouraging to
    us to see is that, on the one hand--you have said, the
    technology is moving quickly. Responsible actors in this space
    are continuing to innovate, but they are not leaving their
    values--and they are concerned about making trustworthy AI--
    behind.
        Smart businesses in this sector, including a lot of the
    leaders, recognize that having trustworthy technology is not
    just good; it is good for business because customers,
    government, other stakeholders have more trust and faith in the
    technology that we are building--that they are building.
        There is a lot to be said for government setting certain
    standards around transparency and asking for folks to show how
    does the technology work at a high level, and how are some of
    those specifications made.
        Mr. Nunn. I would like to continue to see this level of
    collaberation, in the same way we have done with cyber defense
    and bringing an AI consortium together of business leaders like
    yourself, of innovators like yourself, to be able to do this. I
    think that starts with the AI PLAN Act.
        With that, I yield my time.
        Mr. Downing. The gentleman yields.
        The gentlewoman from Colorado, Ms. Pettersen, is now
    recognized for 5 minutes.
        Ms. Pettersen. Thank you, Mr. Chairman.
        Thank you all for being here today and I am grateful that
    you all are hosting this opportunity to talk about such an
    important issue.
        I want to address--Mr. Reisman, you talk about how we need
    to have--it is helpful if we have a regulatory framework at the
    Federal level, and we could have some complementary State laws
    as well.
        I am really worried because we worked in a bipartisan way
    to provide a framework to try to continue to move forward here
    in Congress. We are not known for being the most efficient body
    in government and our inability to come together to produce a
    regulatory framework on AI is concerning, when I look at the
    risks that are involved.
        There is great opportunity for efficiency and a lot of
    promise in all of the different sectors, but I am especially
    worried in the financial sector and the vulnerabilities that we
    have.
        We know that the risks that already--that we are facing are
    going to get exponentially worse with AI. Already, 50 percent
    of fraud has been identified as using AI. Ninety-two percent of
    the financial institutions surveyed indicate that fraudsters
    are using generative AI and 44 percent of financial professors
    report that deepfakes are used in fraudulent schemes.
        So, when I think about the impact to our financial system
    and the gaps that we are leaving here, what are the most
    important steps? This is opened up for all of you. How do we
    provide to ensure that we do not have the gaps in supporting
    our smaller financial systems, so our smaller banks, making
    sure that they have access to the technology and what would you
    recommend on providing that framework and those guardrails at
    the Federal level?
        That is a lot to ask, so do not feel the pressure but if
    anyone wants to add on just some of the key things that we need
    to be thinking about right now.
        Ms. Turner Lee. I will start.
        Ms. Pettersen. Okay.
        Ms. Turner Lee. I definitely think the conversation has
    centered around really advancing national data privacy
    standards and I think that conversation, as someone has already
    mentioned, will sort of ring in the beast of data that is
    actually fueling these systems. If we can come to some
    bipartisan support on that, I think that would be helpful.
        I also think that approaching, Congresswoman, as you said,
    some of these very nuanced areas, like deepfakes--data is out
    there that seniors are thinking that their grandchildren are
    calling them for money. When a senior's economic viability is
    compromised, that is worse for the whole family. I think going
    into those verticals where there is bipartisan agreement is
    another area--we have seen that with the Tools to Address Known
    Exploitation by Immobilizing Technological Deepfakes on
    Websites and Networks (TAKE IT DOWN) Act--in terms of anything
    that has to do with any manipulated content, I think is a step
    forward.
        But I do also think that we have to reimagine what it looks
    like in terms of regulatory framework that has some guidance
    and guardrails, which I would be happy to share with your
    office in more detail.
        Ms. Pettersen. That is great. I look forward to working
    with you on this and we have a bill, as well, that I have
    worked with Representative Flood on; it is the Preventing Deep
    Fake Scams Act.
        This is important because our agencies are only able to
    take on what we are directing them to do and so, when we look
    at trying to change with the times to make sure that they have
    the support they need to address the challenges that we are
    facing now, can you speak to the greater coordination needed
    between the regulators, the industry, and the subject-matter
    experts to develop strategies to protect financial institutions
    and consumers from fraud using AI?
        Ms. Turner Lee. I sure can, if no one else will.
        I think on the deepfake side, I think the challenge that we
    have is that, because we have multiple agencies with
    jurisdiction over this particular area, we have not come up
    with some shared values and goals on, one, how are we defining
    this.
        We have had a lot of conversation, to your point, where we
    have not necessarily agreed on, on the copyright area, this
    digital provenance. The same thing, I think, is actually going
    to go into the deepfake space, where we are looking at
    extracted video, text, and audio and trying to determine, where
    is this coming from? That is very hard for us to track at this
    moment but also coming up with some shared values and goals
    across the various agencies that are responsible for
    enforcement over that.
        I think if we are able to actually get away from some of
    that fragmentation, we could actually push forward with really
    good legislation that protects people from clones.
        Ms. Pettersen. Great.
        Mr. Reisman?
        Mr. Reisman. Yes. I just wanted to say--to praise, in the
    bill you have called for specifically bringing together
    industry and expert stakeholders to talk with regulators and
    government to try and figure out how to solve these problems
    together. It has never been more important, as the technology
    moves quickly, to have all of these voices in the room to
    problem-solve together.
        Ms. Pettersen. It seems like this should just--we should
    bring bills to do this for every agency across--sorry. Thank
    you. I yield my time.
        Mr. Downing. The gentlewoman yields.
        The gentleman from South Carolina, Mr. Timmons, is now
    recognized for 5 minutes.
        Mr. Timmons. Thank you, Mr. Chairman.
        Thank you to the witnesses for being here today.
        I am pleased that the subcommittee is turning its attention
    to the use of artificial intelligence in financial markets.
    Like stablecoins and market structure, this is a technology
    that will have a significant impact on how American companies
    and consumers engage with our evolving financial systems.
        In order for the United States to remain a global leader in
    innovation, we must establish clear rules of the road that
    allow the free market to thrive, and we must do so thoughtfully
    and effectively.
        As I continue to meet with industry leaders, I am
    increasingly concerned about the growing number of conflicting
    State laws related to AI. These laws often vary in scope,
    definition, and enforcement, creating a complex regulatory
    environment for businesses to operate across multiple
    jurisdictions.
        This patchwork of regulation makes it more difficult for
    companies to scale, innovate, and remain compliant, while also
    increasing the risk of inconsistent protections and outcomes
    for consumers. Without a unified approach, we risk slowing
    progress and creating barriers that disadvantage both American
    businesses and the people they serve.
        Mr. Gorfine, States have introduced more than a thousand
    laws related to the use of artificial intelligence. From your
    perspective, what are the most pressing risks of a State-by-
    State regulatory patchwork for both consumers and innovators?
        Mr. Gorfine. It is a good question.
        I want to start by being very clear that when I look at
    this issue I am talking about it within the context of the
    regulated financial services sector. There may be issues
    outside of financial services where States are engaging on AI,
    but I think that what is really important is to recognize that
    there are existing Federal and State laws, there is existing
    regulation, and there is existing guidance that financial
    institutions adhere to where a patchwork of State laws can
    absolutely interfere, conflict, or create ambiguity for
    financial services firms operating on a national level.
        I think that is especially clear in the data context. We
    have existing Gramm-Leach-Bliley Act (GLBA) data privacy laws
    in place for financial services firms. If you start introducing
    a patchwork of State approaches, that can be really
    problematic.
        The same goes for when it comes to risk management. There
    is a very careful risk management framework in place for
    financial services, and, again, I do worry about interference
    of State laws there.
        So that is how I am thinking about the interplay of State
    and Federal, especially in the context of the financial
    services space.
        Mr. Timmons. I am going to turn that a little bit around
    and say, while this is going to be difficult for financial
    services companies to comply and to work across State lines,
    let us talk about the AI companies that are trying to create
    these products.
        I mean, the Chinese are ahead of us or close to being ahead
    of us, and the businesses that they have that are focused on
    this do not have this burden. Would you say that is an equally
    difficult challenge?
        Mr. Gorfine. Yes. I mean, the broader question for AI is
    that I do think having a proper Federal framework that allows
    us to operate on a national scale makes really good policy,
    market, and competitiveness sense. That is something that I
    would absolutely encourage because, as we are describing, it is
    not even just a national competition; it is global, right?
    These activities transcend borders and having a coherent, kind
    of, Federal framework there makes good sense in the AI context.
        Mr. Timmons. Thank you for that.
        While there are many challenges involved in legislating and
    establishing clear rules of the road for artificial
    intelligence, there are also powerful opportunities. Artificial
    intelligence has the potential to equip financial institutions
    with advanced tools to better serve their clients, improve
    efficiency, and reduce risk.
        As I mentioned earlier, one of the most common concerns
    that I hear from both large financial institutions and smaller
    community banks and credit unions is the burden that regulatory
    compliance places on their operations. These requirements
    strain both their financial resources and staffing capacity.
        Artificial intelligence offers a promising solution to help
    automate and streamline compliance processes, allowing firms to
    redirect time and resources toward innovation and customer
    service.
        Dr. Lau, given the significant burden that regulatory
    compliance places on financial institutions of all sizes, how
    is artificial intelligence helping firms streamline these
    processes and manage risk more effectively?
        Mr. Lau. Thank you for the question.
        Definitely, we see applying AI to compliance workflows as
    one of the highest return on investment (ROI) activities that
    we are seeing banks implement today, including smaller
    community banks and regional banks, right? The reason is not
    just so they are able to automate more workflows where you have
    low staffing, but also they open up new opportunities for
    greater compliance. Think about continuous monitoring, 24-hour
    audits, et cetera, right?
        On the flip side, you also need to be able to look at, as
    you are introducing AI to these regulated workflows, what type
    of guardrails or controls are put around those AIs so that they
    are able to follow the right policies and procedures when
    executing those workflows. That, actually, in many cases, is
    the more challenging problem, is, how do you rein in this
    autonomous AI to actually carry out the tasks effectively and
    in compliance?
        Mr. Timmons. Thank you for that.
        Emerging technology has the ability to make sure that the
    U.S. is the center of the global economy for decades to come,
    and we have to get this right.
        With that, Mr. Chairman, I yield back.
        Mr. Downing. The gentleman yields.
        The gentleman from New York, Mr. Torres, is now recognized
    for 5 minutes.
        Mr. Torres. Thank you, Mr. Chair.
        AI is the most transformative technology of our time. The
    rise of generative AI could prove to be as revolutionary as the
    advent of writing or the advent of the printing press. Whether
    AI will create a better world or a worse world, no one knows
    for sure. What we do know for sure is that the world will be
    radically different from anything we have seen before.
        There is nothing new about the use of AI in finance. What
    is new is the use of generative AI, large language models, in
    finance.
        So my first question: What are the capabilities in finance
    that a large language model can perform that legacy AI has been
    historically unable to perform?
        Anyone who can answer that question is free to do so.
        Mr. Lau. I can go ahead and jump in.
        I think Dr. Cox actually mentioned at the beginning, the
    reason why there is so much investment in this space is because
    it is general-purpose AI, meaning that it could actually do
    many different things. You can prompt it in infinitely
    different ways to carry out certain types of workflows that are
    very specific to your day-to-day, all the way to doing things
    like continuous monitoring, et cetera, right?
        So that is the power of the technology, are these general-
    purpose AI models----
        Mr. Torres. What is the best new use--what is the best new
    use case in finance?
        Mr. Lau. Yes. I would say--I mentioned compliance as one of
    the very high ROI activities because of the manual effort
    involved in that but I would say, the most mature one that is
    delivering the highest ROI that we see is around developer
    productivity, the ability for these AI agents to actually go
    and build applications from scratch, empowering even, we are
    seeing, small community banks to leverage these to actually
    really accelerate their own software development and
    integrating their infrastructure stack.
        Mr. Torres. Can an AI banker outperform a human banker?
        Mr. Lau. I think one of the biggest challenges to actually
    having the AI outperform a human banker is to make sure that it
    complies with the common sense of a human banker, right? That
    has actually been challenging when you look at AI agents, to
    give them that type of common sense out of the box, even given
    the vast amounts of data that they have been trained on.
        So building guardrails that a human is trained to follow is
    something that is still an open challenge but something we are
    helping a lot of these banks with today.
        Mr. Torres. Because I know Members of Congress are
    irreplaceable, but I am wondering if bankers and traders are
    replaceable by AI.
        What is the impact of AI on concrete applications like
    credit scoring, loan approvals, fraud prevention and detection?
    Like, does AI lead to more loan approvals or fewer? Does it
    lead to more inclusive credit scoring or less inclusive credit
    scoring? What is the outcome so far.
        Ms. Turner Lee. I can--oh, do you want--well, I can point
    to that.
        I mean, I think we thought, because of the objective nature
    of AI, that it would be easier to see more loan approvals, more
    credit applications actually confirmed----
        Mr. Torres. Where did we get this notion that AI is
    objective?
        Ms. Turner Lee. Well, we thought----
        Mr. Torres. The data comes from the internet.
        Ms. Turner Lee. Well, that is what I was going to say. We
    thought----
        Mr. Torres. It is a reflection of human nature.
        Ms. Turner Lee. That is right and because the data that is
    fueling the AI systems basically comes from us--and
    particularly in generative AI, it is curated data. It is not
    necessarily predictive data. It is data that exists on people
    on the public internet--we are actually in some cases the same
    results and in other instances worse.
        We have seen that also on the housing appraisal side when
    it comes to homeownership, that even if you scrub your entire
    home of any type of remnant or artifact of you, the AI will
    still generate the same results, because AI uses other
    proxies--your address, your net worth in your community, et
    cetera--Congressman.
        So I think, when we say that AI is better than humans or a
    banker can outperform a human, probably in time, but
    expeditious calculation of how we make these decisions comes at
    foreclosing on opportunities economically for various
    consumers.
        Mr. Torres. Can AI--I guess I was going to ask, can AI be
    harnessed to expand access to capital, to expand access to
    credit? One of my frustrations--I have real frustrations with
    traditional credit scoring methodology. Can AI detect new
    patterns of evaluating creditworthiness?
        Mr. Gorfine. I think that is right. I mean, traditional
    credit scores are highly correlated with protected class
    characteristics.
        One thing I would suggest is to consider second-look
    applications of AI. What that means is, if you take an existing
    decline pool and you run the decline pool through, kind of,
    cutting-edge, gen-AI-related underwriting models, you will at
    worst case result in another decline, but you may actually
    start pulling some approvals through that process, and you
    mitigate some of your initial concerns around the impact of
    such models.
        So I think there are ways to smartly start testing these
    models in a way that upholds fairness.
        Mr. Torres. The question for me--because, inevitably, AI is
    going to have some measure of algorithmic bias----
        Ms. Turner Lee. That is right.
        Mr. Torres [continuing]. right? The question for me is not
    whether it is completely free of bias but whether we can make
    it less biased than the human alternative.
        Is that an achievable mission?
        Ms. Turner Lee. Well, I think it is achievable if there is
    transparency, first and foremost, that an AI model is making
    the decision on behalf of the financial--on a financial
    application. Many people do not know that AI is actually being
    used to make those credit decisions, so you have to start with
    public disclosure.
        I think the second thing, to your point, is, we do need a
    stat that is able to actually evaluate what the
    disproportionate impact--disparate impact is.
        Mr. Torres. I am about to be replaced by AI but thank you.
        Mr. Downing. The gentleman's time has expired.
        I would like to thank all the witnesses for your testimony
    today.
        Without objection, all members will have 5 legislative days
    to submit additional written questions for the witnesses to the
    chair. The questions will be forwarded to the witnesses for
    their response.
        Witnesses, please respond no later than October 23, 2025.

        [The information referred to can be found in the appendix.]

        Mr. Downing. With that, this hearing is adjourned.

        [Whereupon, at 3:47 p.m., the subcommittee was adjourned.]

                                [all]

                                    APPENDIX

                                  ----------

                       MATERIALS SUBMITTED FOR THE RECORD
    [GRAPHIC] [TIFF OMITTED] T3432.069

    [GRAPHIC] [TIFF OMITTED] T3432.070

    [GRAPHIC] [TIFF OMITTED] T3432.071

    [GRAPHIC] [TIFF OMITTED] T3432.055

    [GRAPHIC] [TIFF OMITTED] T3432.056

    [GRAPHIC] [TIFF OMITTED] T3432.057

    [GRAPHIC] [TIFF OMITTED] T3432.058

    [GRAPHIC] [TIFF OMITTED] T3432.059

    [GRAPHIC] [TIFF OMITTED] T3432.060

    [GRAPHIC] [TIFF OMITTED] T3432.061

    [GRAPHIC] [TIFF OMITTED] T3432.062

    [GRAPHIC] [TIFF OMITTED] T3432.063

    [GRAPHIC] [TIFF OMITTED] T3432.064

    [GRAPHIC] [TIFF OMITTED] T3432.065

    [GRAPHIC] [TIFF OMITTED] T3432.066

    [GRAPHIC] [TIFF OMITTED] T3432.067

    [GRAPHIC] [TIFF OMITTED] T3432.068

    [GRAPHIC] [TIFF OMITTED] T3432.072

    [GRAPHIC] [TIFF OMITTED] T3432.073

    [GRAPHIC] [TIFF OMITTED] T3432.074

    [GRAPHIC] [TIFF OMITTED] T3432.078

    [GRAPHIC] [TIFF OMITTED] T3432.079

    [GRAPHIC] [TIFF OMITTED] T3432.075

    [GRAPHIC] [TIFF OMITTED] T3432.076

    [GRAPHIC] [TIFF OMITTED] T3432.077

    [GRAPHIC] [TIFF OMITTED] T3432.080

    [GRAPHIC] [TIFF OMITTED] T3432.081

    [GRAPHIC] [TIFF OMITTED] T3432.082

    [GRAPHIC] [TIFF OMITTED] T3432.083

    [GRAPHIC] [TIFF OMITTED] T3432.084

    [GRAPHIC] [TIFF OMITTED] T3432.085

    [GRAPHIC] [TIFF OMITTED] T3432.086

    [GRAPHIC] [TIFF OMITTED] T3432.087

    [GRAPHIC] [TIFF OMITTED] T3432.088

    [GRAPHIC] [TIFF OMITTED] T3432.089

    [GRAPHIC] [TIFF OMITTED] T3432.090

    [GRAPHIC] [TIFF OMITTED] T3432.091

    [GRAPHIC] [TIFF OMITTED] T3432.092

    [GRAPHIC] [TIFF OMITTED] T3432.093

    [GRAPHIC] [TIFF OMITTED] T3432.094

    [GRAPHIC] [TIFF OMITTED] T3432.095

    [GRAPHIC] [TIFF OMITTED] T3432.096

    [GRAPHIC] [TIFF OMITTED] T3432.097

    [GRAPHIC] [TIFF OMITTED] T3432.098

    [GRAPHIC] [TIFF OMITTED] T3432.099

    [GRAPHIC] [TIFF OMITTED] T3432.100

    [GRAPHIC] [TIFF OMITTED] T3432.101

    [GRAPHIC] [TIFF OMITTED] T3432.102

    [GRAPHIC] [TIFF OMITTED] T3432.103

    [GRAPHIC] [TIFF OMITTED] T3432.104

    [GRAPHIC] [TIFF OMITTED] T3432.105

    [GRAPHIC] [TIFF OMITTED] T3432.106

    [GRAPHIC] [TIFF OMITTED] T3432.107

    [GRAPHIC] [TIFF OMITTED] T3432.108

    [GRAPHIC] [TIFF OMITTED] T3432.109

    [GRAPHIC] [TIFF OMITTED] T3432.110

    [GRAPHIC] [TIFF OMITTED] T3432.111

    [GRAPHIC] [TIFF OMITTED] T3432.112

    [GRAPHIC] [TIFF OMITTED] T3432.113

    [GRAPHIC] [TIFF OMITTED] T3432.114

    [GRAPHIC] [TIFF OMITTED] T3432.115

    [GRAPHIC] [TIFF OMITTED] T3432.116