[Senate Hearing 118-779]
[From the U.S. Government Publishing Office]
S. Hrg. 118-779
OVERSIGHT OF AI: INSIDERS' PERSPECTIVES
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HEARING
before the
SUBCOMMITTEE ON PRIVACY,
TECHNOLOGY, AND THE LAW
of the
COMMITTEE ON THE JUDICIARY
UNITED STATES SENATE
ONE HUNDRED EIGHTEENTH CONGRESS
SECOND SESSION
__________
SEPTEMBER 17, 2024
__________
Serial No. J-118-79
__________
Printed for the use of the Committee on the Judiciary
[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]
www.judiciary.senate.gov
www.govinfo.gov
______
U.S. GOVERNMENT PUBLISHING OFFICE
63-050 WASHINGTON : 2026
COMMITTEE ON THE JUDICIARY
RICHARD J. DURBIN, Illinois, Chair
SHELDON WHITEHOUSE, Rhode Island LINDSEY O. GRAHAM, South Carolina,
AMY KLOBUCHAR, Minnesota Ranking Member
CHRISTOPHER A. COONS, Delaware CHARLES E. GRASSLEY, Iowa
RICHARD BLUMENTHAL, Connecticut JOHN CORNYN, Texas
MAZIE K. HIRONO, Hawaii MICHAEL S. LEE, Utah
CORY A. BOOKER, New Jersey TED CRUZ, Texas
ALEX PADILLA, California JOSH HAWLEY, Missouri
JON OSSOFF, Georgia TOM COTTON, Arkansas
PETER WELCH, Vermont JOHN KENNEDY, Louisiana
LAPHONZA BUTLER, California THOM TILLIS, North Carolina
MARSHA BLACKBURN, Tennessee
Joseph Zogby, Chief Counsel and Staff Director
Katherine Nikas, Republican Chief Counsel and Staff Director
Subcommittee on Privacy, Technology, and the Law
RICHARD BLUMENTHAL, Connecticut, Chair
AMY KLOBUCHAR, Minnesota JOSH HAWLEY, Missouri, Ranking
CHRISTOPHER A. COONS, Delaware Member
MAZIE K. HIRONO, Hawaii JOHN KENNEDY, Louisiana
ALEX PADILLA, California MARSHA BLACKBURN, Tennessee
JON OSSOFF, Georgia MICHAEL S. LEE, Utah
JOHN CORNYN, Texas
Cassie Fields, Democratic Chief Counsel
Julissa Milligan Walsh, Republican Chief Counsel
C O N T E N T S
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OPENING STATEMENTS
Page
Blumenthal, Hon. Richard......................................... 1
Hawley, Hon. Josh................................................ 4
WITNESSES
Harris, David Evan............................................... 9
Prepared statement, pp. i-3.................................. 43
Questions submitted with no response returned................ 47
Mitchell, Margaret, Ph.D......................................... 10
Prepared statement........................................... 48
Responses to written questions............................... 68
Saunders, William................................................ 7
Prepared statement........................................... 70
Responses to written questions............................... 74
Toner, Helen..................................................... 5
Prepared statement........................................... 75
Responses to written questions............................... 81
APPENDIX
Items submitted for the record................................... 83
OVERSIGHT OF AI: INSIDERS' PERSPECTIVES
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TUESDAY, SEPTEMBER 17, 2024
United States Senate,
Subcommittee on Privacy, Technology,
and the Law,
Committee on the Judiciary,
Washington, DC.
The Subcommittee met, pursuant to notice, at 2:09 p.m., in
Room 226, Dirksen Senate Office Building, Hon. Richard
Blumenthal, Chair of the Subcommittee, presiding.
Present: Senators Blumenthal [presiding], Klobuchar,
Padilla, Ossoff, Hawley, Kennedy, and Blackburn.
Also present: Senator Durbin.
OPENING STATEMENT OF HON. RICHARD BLUMENTHAL,
A U.S. SENATOR FROM THE STATE OF CONNECTICUT
Chair Blumenthal. This meeting of the Subcommittee on
Privacy, Technology, and the Law will come to order.
I welcome the Ranking Member, and, as well, my colleagues,
Senator Durbin, who is Chair of the Judiciary Committee, and
Senator Blackburn, my partner on the Kids Online Safety Act--
Members of this body who have a tremendous interest in the
topic that brings us here today.
And we are very, very grateful to this group of witnesses
who are among the main experts in the country. And not only
that, but experts of conscience and conviction about the
promise and the dangers of artificial intelligence. We welcome
you, and we thank you for being here.
We've had hearings before in this Subcommittee, seems like
years ago, and, in fact, a short time on artificial
intelligence may seem like years in terms of the progress that
can be made. We've heard from industry leaders responsible for
innovation and progress in AI, and they shared their excitement
for the future. But they also warned about serious risks.
Sam Altman, for example, who sat where you are now, shared
his worst fear that AI could, quote, ``cause significant harm
to the world,'' end quote. But as he sat with me in my office
and described a less advanced version of his technology, he
assured me that there were going to be safeguards, red teams,
all kinds of guardrails that would prevent those dangers.
We're here today to hear from you because every one of the
witnesses that we have today are experts who were involved in
developing AI on behalf of Meta, Google, OpenAI, and you saw
firsthand how those companies dealt with safety issues and
where those companies did well and where they fell short, and
you can speak to the need for enforceable rules to hold these
powerful companies accountable.
And, in fact, Senator Hawley and I have a draft framework
that would impose those kinds of safeguards and guardrails and
impose a measure of accountability. And we are open to hear
from you about ways it can be strengthened, if necessary, or
improved.
But my fear is that we're already beginning to see the
horse out of the barn. Mr. Evans and Mr. Harrison, your
testimony, I think, assured us that the horse was not out of
the barn.
But my fear is that we'll make the same mistake we did with
social media, which is too little, too late. And that's why the
work that Senator Blackburn and I are doing on this online
safety is so important to accomplish with urgency.
Despite those self-professed fears of Sam Altman and
others, Big Tech companies and leading AI companies are rushing
to put sophisticated AI products into the market. The pressure
is enormous. Billions and billions of dollars, careers of
smart, motivated people are on the line. And what seemed to be
a kind of slow walk on AI has turned into literally a gold
rush.
We're in the Wild West and there's a gold rush. The
incentives for a race to the bottom are overwhelming, and
companies, even as we speak, are cutting corners and pulling
back on efforts to make sure that AI systems do not cause the
kinds of harm that even Sam Altman thought were possible.
We're already seeing the consequences.
Generative AI tools are being used by Russia, China, and
Iran to interfere in our democracy. Those tools are being used
to mislead voters about elections and spread falsehoods about
candidates.
So-called face-swapping and nudify apps are being used to
create sexually explicit images of everyone from Taylor Swift
to middle schoolers in our educational institutions around the
country.
One survey found that AI tools are already being used by
preteens to create fake sexually explicit images of their
classmates. That's preteens.
You know, I don't have to expand on this point because
everyone in this room, probably by this point, everybody in
America who is watching or seeing anything on the news has
become familiar with these abuses.
And voice cloning software is being used in imposter
schemes, targeting senior citizens, impersonating family
members, defrauding those seniors of their savings.
This fraud and abuse is already undermining our democracy,
exploiting consumers, and disrupting classrooms. But it's
preventable. It's also a preview into the world that we will
see expanding and deepening without real enforceable rules.
And now, artificial general intelligence, or AGI, which I
know our witnesses are going to address today, provides even
more frightening prospects for harm. The idea that AGI might in
10 or 20 years be smarter, or at least as smart as human
beings, is no longer that far out in the future. It's very far
from science fiction. It's here and now--1 to 3 years has been
the latest prediction, in fact, before this Committee.
And we know that artificial intelligence that's as smart as
human beings is also capable of deceiving us, manipulating us,
and concealing facts from us and having a mind of its own when
it comes to warfare, whether it's cyber war or nuclear war, or
simply war on the ground in the battlefield.
So, we have no time to lose to make sure that those horses
are still in the barn. And I am going to abbreviate the remarks
that I was going to make because we've been joined by a number
of our colleagues, and I want to get to the testimony and give
Senator Hawley a chance to comment.
But let me just say for the benefit of others in this room,
I know our witnesses are familiar with our legislation--that
the principles of this framework include licensing,
establishing a licensing regime, and transparency requirements
for companies that are engaged in high-risk AI development.
It is about oversight, creating an independent oversight
body that has expertise with AI, and works with other agencies
to administer and enforce the law, watermarking--rules around
watermarking and disclosure when AI is being used, enforcement
ensuring that AI companies can be held liable when their
products breach privacy, violate civil rights or cause other
harm.
And I will just emphasize the last of these points,
enforcement, for me as a former law enforcer, I served as
attorney general in my State and the Federal prosecutor, U.S.
attorney, for most of my career is absolutely key. And I think
to Senator Hawley, as well, as a former attorney general, and
to many others, like Senator Klobuchar, who also was an
enforcer.
I'm very hopeful that we can move forward with Senator
Klobuchar's bill on election security. She's done a lot of work
on it, and it's an excellent piece of legislation. And I salute
her for her leadership, as well as Senator Durbin's and Senator
Coons' bill on deepfakes.
We have a number of proposals like them that are ready to
become law, and I hope that a hearing like this one will
generate the sense of urgency that I feel for my colleagues, as
well.
You can see from the Membership today that it's bipartisan.
Not just Senator Hawley and myself, but literally bipartisan, I
think, across the board, just as the vote on the Kids Online
Safety Act was 91-to-3 in the United States Senate to approve
it. I think we can generate the same kind of overwhelming
bipartisan support for these proposals.
And finally, what we should learn from social media, that
experience is, don't trust Big Tech. And I think most of you
very explicitly agreed, we can't rely on them to do the job.
For years, they said about social media, ``Trust us.''
We've learned we can't and still protect our children and
others.
And as one of you said, they come before us, they say all
the time, we're in favor of regulation, just not that
regulation. Or they have other tricks that they are able to
move forward with--the armies of lobbyists and lawyers that
they can muster.
So, we ask for their cooperation. I challenge Big Tech to
come forward and be constructive here. They've indicated they
want to be.
But some kind of regulation to control and safeguard the
people of the world, it's not just America, have to be adopted.
And I hope that this hearing will be another step in that
process. And I turn to the Ranking Member.
OPENING STATEMENT OF HON. JOSH HAWLEY,
A U.S. SENATOR FROM THE STATE OF MISSOURI
Senator Hawley. Thank you very much, Mr. Chairman. Thanks
for your leadership on this over this entire Congress. It's
been a real pleasure to work with you. Thanks to our witnesses
for being here.
I don't really have much to add to Senator Blumenthal's
outstanding opening statement other than just to observe, that
we have had many executives sit where our witnesses today are
sitting--many avid proponents of this AI revolution that we're
in the midst of.
And we've heard a lot of promises to this Subcommittee from
those executives, who I might just point out always seem to
have a very significant financial interest in what they're
saying, be that as it may. But they have given us all kinds of
rosy predictions. AI is going to be wonderful for this country.
It's going to be fantastic for the workers of this country.
It's going to be amazing for normal workaday Americans.
Well, I think today's hearing is particularly interesting
and particularly important because today we start to test those
promises. We have in front of us folks who've been inside those
companies who have worked on these technologies, who have seen
them firsthand, and I might just observe, don't have quite the
vested interest in painting that rosy picture and cheerleading
in the same way that some of these other executives have.
So, I want to particularly thank you for being here today.
Our witnesses, thank you for being willing to speak up, and
thank you for being willing to give the American people a
window into what's actually happening with this technology.
I think the testimony you're about to offer is so
important, and I think it will help us realize, understand
where this technology is, what the challenges are that we are
facing, and also, I hope help us, as Senator Blumenthal alluded
to, legislate in a way that will actually protect the American
people, which is our charge in all of this. Thank you, Mr.
Chairman.
Chair Blumenthal. Thanks, Senator Hawley. I haven't asked
Senator Durbin whether he would like to make an opening
statement. Evidently not. So let me just introduce the
witnesses.
Helen Toner is a policy reach researcher at Georgetown
University's Center for Security and Emerging Technology, where
her work focuses on AI safety, U.S. China competition, and
national security. She previously worked as a senior research
analyst at Open Philanthropy, where she advised policymakers
and grant makers on AI policy and strategy. Alongside her work
at CSET, she served on OpenAI's nonprofit board of directors
from 2021 to 2023.
David Evan Harris is a Chancellor's Public Scholar at the
University of California, Berkeley, and faculty member at the
Haas School of Business, where he teaches courses on artificial
intelligence, ethics, social movements and social media, and
civic technology.
In addition to teaching, he conducts research on AI and
governance and serves as an advisor to the California
Initiative for Technology and Democracy, and a number of other
organizations focused on technology and policy.
Mr. Harris has advised the White House, U.S. Congress,
European Union, United Nations, NATO, and the California
Legislature about technology policy. His writings and
commentary have been featured by publications too numerous to
mention here.
Margaret Mitchell is a computer scientist who works on the
ethical development of AI systems within the tech industry. She
has held researcher positions at Microsoft and Google, and her
pioneering work on model cards, now common across the tech
industry, has been recognized by the Secretary of Defense, at
that point Ash Carter, as an outstanding innovation for public
good.
She currently works at Hugging Face as a researcher and
chief ethics scientist driving forward work on ML data
processing, responsible AI development, AI ethics, and she's
appearing today in her personal capacity.
William Saunders worked at OpenAI for 3 years on the
Alignment Team, which later became the Superalignment Team,
researching techniques to help humans understand and control AI
systems. He resigned in February 2024, concerned that OpenAI
was not adequately preparing for the advanced AI systems the
company is trying to build.
So as Senator Hawley very, very aptly said, you are
insiders who have the courage to come forward, and we are very
grateful to you. As is our custom, I would ask you to rise and
I'll administer the oath.
[Witnesses are sworn in.]
Chair Blumenthal. Very good, thank you. We'll just begin
and go down the panel beginning with Ms. Toner.
STATEMENT OF HELEN TONER, DIRECTOR, STRATEGY
AND FOUNDATIONAL RESEARCH GRANTS, CENTER
FOR SECURITY AND EMERGING TECHNOLOGY,
GEORGETOWN UNIVERSITY, WASHINGTON, DC
Ms. Toner. Chair Blumenthal, Ranking Member Hawley, Members
of the Subcommittee, thank you for the opportunity to testify
today. I want to start by commending you on the depth and
sustained focus that this Subcommittee is bringing to the many
and varied challenges and opportunities that we face with AI.
My work has focused on AI policy for the last 8 years, with
an emphasis on national security, US-China competition, and AI
safety.
In 2019, I moved to Washington to help found CSET, the
Center for Security and Emerging Technology at Georgetown
University, which has since grown to be a highly respected
source of analysis on AI policy issues.
While working full-time at CSET, I also spent 2\1/2\ years
serving on OpenAI's nonprofit board from 2021 until the widely
covered events of last November.
Today, I'll be drawing on my research at CSET, my
experiences on the board, and my extensive relationships with
and interactions with AI researchers and executives from the
years that I've worked in this space.
The title of this hearing is ``Oversight of AI: Insiders'
Perspectives,'' and the biggest disconnect that I see between
AI insider perspectives and public perceptions of AI companies
is when it comes to the idea of artificial general
intelligence, or ``AGI.''
This term, AGI, isn't well-defined, but it's generally used
to mean AI systems that are roughly as smart or capable as a
human.
In public and policy conversations, talk of human-level AI
is often treated as either science fiction or marketing hype.
But many top AI companies, including OpenAI, Google, Anthropic,
are treating building AGI as an entirely serious goal and a
goal that many people inside those companies think they might
reach in 10 or 20 years, and some believe could be as close as
1 to 3 years away.
More to the point, many of these same people believe that
if they succeed in building computers that are as smart as
humans or perhaps far smarter than humans, that technology will
be at a minimum extraordinarily disruptive, and at a maximum
could lead to literal human extinction.
The companies in question often say that it's too early for
any regulation because the science of how AI works and how to
make it safe is too nascent.
I'd like to restate that in different words.
They're saying, we don't have good science of how these
systems work or how to tell when they'll be smarter than us. We
don't have good science for how to make sure they won't cause
massive harm. But don't worry, the main factors driving our
decisions are profit incentives and unrelenting market pressure
to move faster than our competitors. So, we promise we're being
extra, extra safe.
Whatever these companies say about it being too early for
any regulation, the reality is that billions of dollars are
being poured into building and deploying increasingly advanced
AI systems. And these systems are affecting hundreds of
millions of people's lives, even in the absence of scientific
consensus about how they work or what will be built next.
So, I would argue that a wait-and-see approach to policy is
not an option. I want to be clear. I don't know how long we
have to prepare for smarter-than-human AI, and I don't know how
hard it will be to control it and ensure that it's safe.
As I'm sure the Committee has heard a thousand times, AI
doesn't just bring risks. It also has the potential to raise
living standards, help solve global challenges, and empower
people around the world. If the story were simply that this
technology is bad and dangerous, then our job would be much
simpler.
The challenge we face is figuring out how to proactively
make good policy despite immense uncertainty and expert
disagreement about how quickly AI will progress, and what
dangers will arise along the way.
The good news is that there are light touch adaptive policy
measures we can adopt today that can both be helpful if we do
see powerful AI systems soon, and also be helpful with many
other AI policy issues that I'm sure we'll be discussing today.
I want to briefly highlight six policy building blocks that
I describe in more detail in my written testimony.
First, we should be implementing transparency requirements
for developers of high-stakes AI systems. We should be making
major research investments in how to measure and evaluate AI,
as well as how to make it safe. We should be supporting the
development of a rigorous third-party audit ecosystem,
bolstering whistleblower protections for employees of AI
companies, increasing technical expertise in Government, and
clarifying how liability for AI harms should be allocated.
These measures are really basic first steps that would in
no way impede further innovation in AI. This kind of policy is
about laying some minimal commonsense groundwork to help us get
a handle on AI harms we're already seeing. And also set us up
to identify and respond to new developments in AI over time.
This is not a technology we can manage with any single
piece of legislation. But we're long overdue to implement some
of these basic building blocks as a starting point. Thank you
and I look forward to your questions.
[The prepared statement of Ms. Toner appears as a
submission for the record.]
Chair Blumenthal. Thanks very much, Ms. Toner. Mr.
Saunders.
STATEMENT OF WILLIAM SAUNDERS, FORMER MEMBER, TECHNICAL STAFF,
OPENAI, SAN FRANCISCO, CALIFORNIA
Mr. Saunders. Mr. Chairman, Ranking Member Hawley, and
distinguished Members, thank you for the opportunity to address
this Committee.
For 3 years, I worked as a member of technical staff at
OpenAI. Companies like OpenAI are working toward building
artificial general intelligence, AGI. They're raising billions
of dollars toward this goal.
OpenAI's charter defines AGI as highly autonomous systems
that outperform humans at most economically valuable work, that
this means AI systems that could act on their own over long
periods of time and do most jobs that humans can do.
AI companies are making rapid progress toward building AGI.
A few days before this hearing, OpenAI announced a new system
GPT o1 that passed significant milestones, including one that
was personally significant for me.
When I was in high school, I spent years training for a
prestigious international computer science competition.
OpenAI's new system leaps from failing to qualify to winning a
gold medal doing better than me in an area relevant to my own
job. There are still significant gaps to close, but I believe
it is plausible that an AGI system could be built in as little
as 3 years.
AGI would cause significant changes to society, including
radical changes to the economy and employment. AGI could also
cause catastrophic harm via systems autonomously conducting
cyber-attacks or assisting in the creation of novel biological
weapons.
OpenAI's new AI system is the first system to show steps
toward biological weapons risk as it is capable of helping
experts in planning to reproduce a known biological threat.
Without rigorous testing, developers might miss this kind
of dangerous capability. While OpenAI has pioneered aspects of
this testing, they've also repeatedly prioritized speed of
deployment over rigor. I believe there is a real risk they will
miss important dangerous capabilities in future AI systems.
AGI will also be a valuable target for theft, including by
foreign adversaries of the United States. While OpenAI publicly
took claims to take security seriously, their internal security
was not prioritized.
When I was at OpenAI, there were long periods of time where
there were vulnerabilities that would've allowed me, or
hundreds of other employees at the company, to bypass access
controls and steal the company's most advanced AI systems,
including GPT-4.
No one knows how to ensure that AGI systems will be safe
and controlled. Current AI systems are trained by human
supervisors giving them a reward when they appear to be doing
the right thing. We will need new approaches when handling
systems that can find novel ways to manipulate their
supervisors or hide misbehavior until deployed.
The Superalignment Team at OpenAI was tasked with
developing these approaches, but ultimately, we had to figure
out as we went along--a terrifying prospect when catastrophic
harm is possible.
Today, that team no longer exists. Its leaders and many key
researchers resigned after struggling to get the resources they
needed to be successful.
OpenAI will say that they're improving. I, and other
employees who resigned, doubt they will be ready in time. This
is true, not just with OpenAI, the incentives to prioritize
rapid deployment applies to the entire industry. This is why a
policy response is needed.
My fellow witnesses and I may have different specific
concerns with the AI industry, but I believe we can find common
ground in addressing them. If you want insiders to communicate
about problems within AI companies, you need to make such
communications safe and easy. That means a clear point of
contact and legal protections for whistleblowing employees.
Regulation must also prioritize requirements for third-
party testing, both before and after deployment. Results from
these tests must be shared.
Creating an independent oversight organization and mandated
transparency requirements, as in Senator Blumenthal and Senator
Hawley's proposed framework, would be important steps toward
these goals.
I resigned from OpenAI because I lost faith that by
themselves they will make responsible decisions about AGI. If
any organization builds technology that imposes significant
risks on everyone, the public must be involved in deciding how
to avoid or minimize those risks.
That was true before AI. It needs to be true today with AI.
Thank you for your work on these issues and I look forward to
your questions.
[The prepared statement of Mr. Saunders appears as a
submission for the record.]
Chair Blumenthal. Thanks very much, Mr. Saunders.
By the way, I'm going to ask that all of your written
testimony be made a part of the record. So I know you've
abbreviated your remarks, as I expect the others will do, as
well. Your full remarks will be part of the record, without
objection. Mr. Harris.
STATEMENT OF DAVID EVAN HARRIS, SENIOR POLICY
ADVISOR, CALIFORNIA INITIATIVE FOR TECHNOLOGY
AND DEMOCRACY, AND CHANCELLOR'S PUBLIC SCHOLAR
AT THE UNIVERSITY OF CALIFORNIA, BERKELEY;
SAN FRANCISCO, CALIFORNIA
Mr. Harris. Chairman Blumenthal, Ranking Member Hawley, and
Members of the Committee, it is an honor to appear before you
today to discuss the harms and risks of artificial
intelligence.
It is particularly heartening to see that this Committee
has developed a promising bipartisan framework and proposals
that I earnestly hope will become law. They are urgently needed
to provide effective guardrails for AI systems.
My name is David Evan Harris. From 2018 to 2023, I worked
at Facebook and Meta on the Civic Integrity and Responsible AI
Teams. In my role, I helped lead efforts to combat online
election interference, protect public figures, and drive
research to develop ethical AI systems and AI governance.
Today, those two safety teams do not exist.
In the past 2 years, there have been striking changes
across the industry. Trust and safety teams have shrunk
dramatically. Secrecy is on the rise, and transparency is on
the decline.
Since leaving Meta, I have helped craft two bills about
deepfakes and elections in California that await the Governor's
signature. Working closely with policymakers in California,
Arizona, and internationally, I am more convinced than ever
that effective oversight of AI is possible.
Today, there are three things that I hope you take away
from my testimony.
First, voluntary self-regulation does not work.
Second, many of the solutions for AI safety and fairness
already exist in the framework and bills proposed by the
Members of this Committee.
Third, as you said, not all the horses have left the barn.
There is still time to act.
Back to my first point: Voluntary self-regulation is a
myth. Take just one example from my time at Facebook. In 2018,
the company set out to make time on their platforms into time
well spent: reducing the number of viral videos and increasing
more meaningful content from friends and family.
The voluntary policy opened up a vacuum that TikTok was
more than happy to step into.
Today, Facebook and Instagram are fighting to claw back
market share from TikTok with reels, essentially, those same
viral videos that they sought to diminish.
When one tech company tries to be responsible, another less
responsible company steps in to fill the void. While nonbinding
efforts such as the White House Voluntary AI Commitments and
the AI Elections Accord are positive steps forward, the reality
is that we've seen very little clear progress toward the
promises made by tech companies in those commitments.
When it comes to policies and laws governing AI, laws with
``shalls'' rather than ``mays'' are essential. Without the
``shalls,'' the legislation becomes voluntary and many
companies will delay or simply avoid taking meaningful actions
to prioritize safety or harm reduction.
To my second point: We don't need silver bullets. The
framework proposed by this Committee's leadership already has
so many of the answers.
Two recommendations, in particular, in the framework are
essential components for legislation: AI companies should be
held liable for their products, and they should be required to
embed hard-to-remove provenance data in AI-generated content.
It is encouraging to see a bill on transparency in
elections that would require labeling of some AI-generated
material. More steps like these are needed.
This brings me to my final point: The horses have not left
the barn. The misconception is that it is too late to do
anything. It can be dizzying to watch the fast-paced releases
of AI, voice and image, deepfakes, and the growing role of
biased AI systems making decisions about our lives.
But there are still so many more uses of AI technology that
have not yet seen the light of day.
Next come the realistic video deepfakes, live audio
deepfakes that can interact with millions of people at once,
personalized election disinformation calls, large scale
automated sextortion schemes targeting children. Those are just
a few of the ones that we see on the horizon.
We need to move quickly with binding and enforceable
oversight of AI. It is possible.
If you take action now on the promising framework and bills
already before you, you can reign in the Clydesdales and the
centaurs waiting just behind the barn door. Thank you.
[The prepared statement of Mr. Harris appears as a
submission for the record.]
Chair Blumenthal. Thank you very much, Mr. Harris. Ms.
Mitchell.
STATEMENT OF MARGARET MITCHELL, PH.D., FORMER STAFF RESEARCH
SCIENTIST, GOOGLE AI, SEATTLE, WASHINGTON.
Dr. Mitchell. Chairman Blumenthal, Ranking Member Hawley,
and Members of the Subcommittee, thank you for the opportunity
to testify here today.
My name is Margaret Mitchell and I'm here in my capacity as
a computer scientist and researcher who has worked in the tech
industry for over 10 years. My Ph.D. is in natural language
generation, once a niche area, but currently a topic of intense
interest all over the world.
As a student eating ramen at a State school in Washington,
I couldn't imagine that my work would have the relevance that
brings me here today.
I've had the privilege of being a researcher at Microsoft
and Google, and I'm currently chief ethics scientist at AI
startup Hugging Face.
I'm grateful that I've had the opportunity to work with
some of the world's brightest minds in pursuit of creating
beneficial technology that can aid and assist people in their
work and everyday lives.
I began working on ethical AI around 2015, as neural
networks began to show clear signs of working well for the
tasks I cared about. While they could be used to provide
beneficial technology, such as visual descriptions for people
who are blind, they were entangled with a host of troubling
issues.
For example, the amount of data they required brought with
it concerning questions for me about how the data was being
collected and how data biases were being accounted for.
These issues were difficult to address within the tech
industry. It was too easy to overlook the connection between
what we developed and the foreseeable harms to people once
deployed. Too easy to gather more and more data while ignoring
issues of consent, credit, and compensation.
We were on a path of creating systems that we didn't fully
understand, enabled by data sets that we hadn't critically
analyzed, and motivated by evaluations that didn't critically
engage with easily foreseeable contexts of use.
When I joined Google in 2016, I was highly motivated to try
and change this. One of the basic ideas I had was the role of
foresight, of thinking through all the different ways that
technology might evolve and using this to develop technology
that's as beneficial as possible, while mitigating foreseeable
risks before they occur.
But this type of thinking is difficult to incorporate into
standard development practices, where internal incentives push
developers to launch new products quickly without productive
collaboration across different ideas, viewpoints, and
critiques.
As I discuss these issues with my friends across the
industry, it seemed this problem was common, present throughout
large tech companies. Because of this, I realized that ethical
AI practices could be more successful if I could turn those
practices into launches.
And so, with my colleagues, we introduced model cards,
launchable artifacts that incentivize thinking about
technologies impacts, including documenting the intended use of
a model and evaluating its performance across subpopulations
that could foreseeably be subject to unfair treatment from an
AI system.
Part of my work on documentation and the role of critical
thinking and foresight and development, made it clear to me
that there should be some amount of due diligence before a
system is developed, such as research to inform predictions on
what the system will be like by reviewing past literature and
work from related fields.
This voted me to co-author a now famous paper called, ``On
the Dangers of Stochastic Parrots: Can Language Models Be Too
Big? ''
I've since continued my work trying to understand gaps in
AI development that overlook the impacts of the technology and
developing methods to address them.
I've taken on new challenges in understanding when systems
might be more open or more closed, and now work at AI startup
Hugging Face, a company focused on open science where I operate
in a culture of transparency and collaboration across different
viewpoints, and, for the first time, can go deeply into how the
AI world I'm most familiar with connects to AI policy.
My journey has made clear some of the ways that the
Government might be able to help shape AI development within
the tech sector, so that it may be positive for the public.
Briefly, this involves filling key gaps in AI development
practices and research, including, one, research on the
relationships between model inputs and model outputs.
Two, rigorous analysis of data models and systems.
Three, implementing due diligence and foresight, including
before development.
And four, operationalizing transparency in the AI
lifecycle, including after deployment. Potential solutions
primarily take two forms: Federal research funding, and
requiring documentation and disclosure.
And I echo my colleagues here in advocating for increased
support for whistleblowing.
I greatly appreciate the work that so many Members of
Congress, congressional staff, Federal agencies, and my
research and private sector colleagues are doing to advance the
science and practice of ethical AI. I look forward to
discussing these important topics with you today and welcome
your questions.
[The prepared statement of Dr. Mitchell appears as a
submission for the record.]
Chair Blumenthal. Thank you very much, Ms. Mitchell.
Again, I want to thank all of you for your very insightful
and important testimony. We're going to have 5-minute rounds of
questions, and I'll begin.
Mr. Saunders, you--I'm sorry.
Mr. Harris, you said in your testimony, quote, ``Trust and
safety teams have shrunk dramatically,'' secrecy and lack of
transparency have increased. Is that true across the board of
all companies?
Mr. Harris. Thank you so much, Senator Blumenthal, for the
opportunity to answer that question.
I have appended in the appendices to my written testimony,
a report by an organization called The Free Press that is
entitled, ``The Big Tech Backslide.''
I would encourage you and your colleagues to visit this
report. It's the most comprehensive study that I have seen so
far about the backslide--the backslide consistent part of just
removing the people from those teams.
It has been reported that Elon Musk let go of 81 percent of
the staff of Twitter after purchasing it.
We saw large layoffs across the board in the entire sector,
but part of the backslide is also the retreat from policies,
specific policies that the companies have designed to keep our
elections safe.
So I do believe that I personally have observed one company
that has actually made strides forward and hired many people
during this same period of the past 2 years. And you're not
going to like the one that it is, but it's TikTok. Honestly,
TikTok has been hiring a lot of the people who were let go from
many of those other companies.
Chair Blumenthal. But the measure of their commitment to
some oversight and safeguards is their investment in that kind
of, in effect trust and safety teams, and they have been
backsliding, which belies their stated commitment to ``Trust
us, we'll do it, don't worry.''
Mr. Harris. Yes. perhaps because my equine metaphor
resonated so much with you, I could offer another story from
the animal kingdom, but this time a sign in nature. There is a
metaphor that is used in the tech industry that I know of being
used in at least two of the very biggest tech companies, and
it's called, the metaphor of the bear.
You, Senators, are the bear in this metaphor, along with
other regulatory bodies--the regulators. And the tech companies
in this metaphor are people running away from the bear, as fast
as they can. In this story the bear eventually catches up and
it eats the slowest person, eating the slowest tech company.
Thus, the moral of the story, if you are a tech company,
is, just don't be the second slowest. That is the strategy. So
as long as you can point to someone, to another tech company
that is doing a worse job than you are, on trust and safety,
the idea is that is the optimal allocation of resources in your
company.
Chair Blumenthal. I think what's important here is that all
of the tech companies, not just one, not just, for example,
OpenAI, we're not singling out one or another, are engaged in
that kind of strategy.
Let me ask Ms. Toner, you wrote in your testimony that
your, quote, ``experience on the board of OpenAI taught me how
fragile internal guardrails are when money is on the line and
why it's imperative that policymakers step in.''
I know there are limits on what you can discuss publicly,
I'm very respectful of them. But maybe you can tell us what you
had in mind when you wrote that sentence.
Ms. Toner. Certainly, Senator. And there's now been enough
public reporting of some of the kinds of incidents that
demonstrate this dynamic, including situations like process
known as the deployment safety board, which was set up
internally.
Great idea for something to try, to try and coordinate
safety between OpenAI and Microsoft when using OpenAI products.
It's now been reported publicly that in the early days of that
process, Microsoft--they're in the midst of planning a very
big, very important launch, the launch of GPT-4--and Microsoft
went ahead and launched GPT-4 to tens of thousands of users in
India, without getting approval from that deployment safety
board.
Another example would be that there's been, since I stepped
off the board, there's been concerns raised from inside the
company that in the lead up to the launch of their 4o model,
which was the, you know, voice assistant that had, you know,
very, very exciting launch videos, was launched the day before
a Google event that OpenAI knew might upstage them.
And there's been concerns raised from inside the company
about their inability to fully carry through the kinds of
safety commitments that the company had made in advance of
that.
So there's, you know, there's additional examples, but I
think that those two illustrate the core point.
Chair Blumenthal. Thank you. I'm going to be hopeful we'll
have another round of questions, but I'm going to be respectful
about my 5-minute rule and turn to the Ranking Member.
Senator Hawley. Thank you, Mr. Chairman.
Ms. Toner, I just want to stay with you and maybe pick up
there. My understanding is that when you left the OpenAI board,
one of the reasons that you did so is you felt you couldn't do
your job properly, meaning you couldn't effectively oversee Mr.
Altman and some of the safety decisions that he was making.
You had said this year, that, I'm just going to quote you,
that ``Mr. Altman gave inaccurate information about the small
number of formal safety processes that the company did have in
place.'' That is, he gave incorrect information to the board.
To the extent you're able, can you just elaborate on this?
I'm interested in what's actually being done for safety inside
this company, in no small part because of what he told us when
he sat where you're sitting.
Ms. Toner. Thank you, Senator. Yes, I'm happy to elaborate
to the extent that I can without breaching any confidentiality
obligations. I believe that, you know, when the company has
safety processes, they announce them loudly and proudly. So I
believe that you and your staff would be aware of the processes
they have in place.
At the time, you know, one that I was thinking of, which
was one of the first formal processes that I'm aware of, was
this deployment safety board that I just discussed. And this
breach by Microsoft that took place in the early days there.
Since then, they have introduced a preparedness framework
which I think is, you know--I want to commend many of these
companies for taking some good steps. I think the idea behind
the preparedness framework is good, I think to the extent they
execute on it, that's great.
But there have been concerns raised about, you know, how
well they're able to comply with it. Then also, it's been
publicly reported that the, you know, really respected expert
they brought in to run that team has since been reassigned from
that role, which, you know, I worry what that means for, for,
you know, the influence that that team is able to--to exert on
the rest of the company.
And I think that is illustrative, as well, of a larger
dynamic that I'm sure all of the witnesses here today have
observed, which is, there are really great people inside all of
these companies trying to do really great things.
And the challenge is that if everything is up to the
companies themselves, and to the leadership teams who are
needing to make tradeoffs around getting products out, making
profits, attracting new investors, those teams may not get the
resourcing, the time, the influence, the, the ability to
actually shape what happens that they need.
So I think, you know, many of the--many of the dynamics
that I witnessed echo very much what I'm hearing from my fellow
witnesses.
Senator Hawley. That's very helpful. Let me just ask you
this, let me just put a finer point on it because Mr. Altman,
as I said, testified to us on this connection this past year.
Here's what--part of what he said. ``We make,'' meaning
OpenAI, ``significant efforts to ensure that safety is built
into our systems at all levels.''
And then he went on to say, and I'm still quoting him,
``Before releasing any new system, OpenAI conducts extensive
testing, engages external experts for detailed reviews and
independent audits, improves the model's behavior, and
implements robust safety and monitoring systems.'' In your
experience, is that accurate?
Ms. Toner. I believe it is possible to characterize the
company's activities accurately that way. Yes. The question is,
how much is enough? Who is making those decisions? And what
incentives are driving those decisions?
So in practice, you know, if you make a commitment, you
have to write that commitment down in some words, and then when
you go to implement it, there's going to be a lot of detailed
decisions you have to make about what information is shared
with whom, at what time, who is brought into the right room to
make a certain decision.
Is your safety team, you know, whatever kind of safety team
it might be, are they brought in from the very beginning to
help with conception of the product, really think from the
start about, you know, what implications this might have. Or
are they handed something a couple of weeks before a launch
deadline and told, okay, make this as good as you can do?
Here, I'm not trying to refer to any specific incidents at
OpenAI. I'm really referring to, again, you know, examples that
I've heard reported publicly, heard from across the industry,
that there are--there are good efforts and I worry that we
should not--if we rely on the companies to make all of those
tradeoffs, all of those detailed decisions about how those
commitments are implemented, that they're unable--they're just
unable to fully account for the interests of a broad public.
And I think, you know, you hear this, as well, from people,
you know, I've heard this from people in multiple companies,
you know, sentiment along the lines of, please, like, please
help us slow down. Please give us guardrails that we can point
to that are external, that help us not only be subject to these
market pressures.
Senator Hawley. Just in general, is your impression now,
is, was OpenAI doing enough in terms of its safety procedures
and protocols to adequately vet its own products and to protect
the public?
Ms. Toner. I think it depends entirely on how rapidly their
research progresses. If their most aggressive predictions of
how more--how quickly their systems will get more advanced are
correct, then I have serious concerns, if they--their
predictions--their most aggressive predictions may well be
wrong, in which case I'm somewhat less concerned.
Senator Hawley. Let me finally, because I want to be
mindful of the time and I've got colleagues who want to ask
questions, let me just end with this.
You--in your written testimony, you make, I think, a very
important and helpful point about AI development in China and
why the competition with China, though real, should not be
taken as an excuse for us to do nothing.
Could you just amplify that? Because we've heard a lot of
folks sitting where you're sitting over the last year and a
half, raise the China point and usually say, well, we mustn't--
we mustn't lose the race to China, therefore it would be better
if Congress did little to nothing. You think that that's wrong?
Just explain this to us and why.
Ms. Toner. I think that the competition with China,
certainly a very important consideration, and we should be
keeping a very close eye on what they're doing and how U.S.
technology compares to their technology.
But I think it is used as an all-purpose excuse to not
regulate, and an all-purpose defense against any kind of
regulation. I think that's mistaken on a few fronts. It's
mistaken because of what's happening in China. They are
regulating their sector pretty heavily. They are, you know,
scrambling to keep up with the U.S.
They are facing some serious macro headwinds in terms of
economic problems, access to semiconductors after U.S. export
controls. So China, you know, has its own set of issues, we
shouldn't treat them as, you know, just absolutely raring to go
and about to pass us at any moment.
And I think it also totally belies the fact that regulation
and innovation do not have to be intention. This is a
technology, AI, that consumers don't trust. There's been recent
consumer sentiment surveys showing that if they see AI in a
product description, they're less likely to use the product.
So if you can implement regulation that is, that is light
touch, that increases consumer trust, that helps the Government
be positioned to understand what is going on with the
technology. You can, you can regulate in really sensible ways
without even impacting innovation at all. So, it's irrelevant,
you know, if it's going to affect the race with China.
Chair Blumenthal. Thanks very much, Senator Hawley. Senator
Durbin.
Chair Durbin. Thank you. I'm going to ask some basic
questions. Liberal arts lawyer----
[Laughter.]
Chair Durbin. Forgive me, maybe that will be, lead to
inspiration, I'm not sure.
The--I'm trying to understand the mechanism of regulation
in the AI venture.
We look back in history to the Manhattan Project, and it
was inspired by the Government, funded by the Government,
staffed by the Government, built by the Government, and
succeeded. I think it generally succeeded.
Then you fast forward a few years to the race to the moon,
and the question is, was it inspired by the Government? But is
now--do we have more private sector involved in it in terms of
the actual project and its success?
Then you go to the world of quantum. And let me tell you,
at this point, that's as far as I can go in my expertise, other
than the fact that I met a man who works for DARPA who
explained to me that his job is to prove that the quantum
effort of a certain company, private company doesn't work. He's
supposed to prove this over and over again. His name is
Altepeter--very interesting guy.
But it appears that that is all private sector, and we, as
regulators, are on the outside getting ready to prove it
doesn't work, hoping that someday we won't be able to prove
that.
So the question is now, where is AI? And can we, as a
Government, actually regulate the future of an industry unless
we have a team and technology that matches what they have--or
at least as close to it?
It seems to me, it's tough to regulate an entity if you
don't understand it and the decisions that they have to make.
Would anybody like to straighten me out? Please.
Dr. Mitchell. Yes, thank you for the question. I think that
the Government has a really useful role to play here in
incentivizing good work. So in my written testimony, I speak to
how there are very clear gaps in current AI development
processes within the tech industry where there just isn't
enough research and there isn't enough work, and it hasn't
organically emerged due to market pressures, and things like
that.
So, in particular, there isn't a really rigorous mature
science of data analysis in order to understand the
relationship between inputs and outputs. We've seen in the past
that there have been grants, things available through DARPA,
for example, to increase the state-of-the-art in machine
translation. So now it's possible to communicate with someone
speaking in a different language without knowing that
language--that has been partially enabled by DARPA and the
grants through there.
So I think that by focusing in on these, on these, sort of,
difficult points within the AI lifecycle where there really
hasn't been development, and there should be, and I think we
cover that in some of our written testimonies, the Government
has a very good role to play in helping make sure that AI
continues to be developed in a way that's beneficial, as
opposed to overlooking a lot of the serious issues.
Chair Durbin. The way you explained your background, at
least, I hope I caught it, you've been primarily private
sector.
Dr. Mitchell. Yes.
Chair Durbin. So the question I'm asking you is, if the
Government is going to regulate you with some Federal employees
or contract employees, do we have the level of expertise to
really interface with what you're doing in the private sector?
Dr. Mitchell. I think it's possible to hire for the
expertise, if you don't already. I mean, I will speak to that,
as I've met with staff, I've been incredibly impressed by the
intelligence of the people that I've operated with. So I do
have some faith that the Government is able to employ relevant
people.
I think part of the difficulty is the compensation. Right?
So Big Tech companies are offering million-dollar packages. So
that ends up being, I think, quite a difficult tension there.
But this is a situation where you might have, you know,
individuals in Government under NDA, working within the company
or auditing the company in some way, you know, respecting trade
secrets, IP, those kinds of things. But still being able to
sort of, you know, lift the hood and see what's happening
underneath and provide feedback on it.
Chair Durbin. Anyone else want to comment on the Government
effort? Mr. Harris?
Mr. Harris. Yes. Thank you so much for the question,
Senator Durbin.
I understood two different elements from your question.
One, being, can we actually regulate this? And two, being, do
we have the right people in Government to do it?
To the first point, I would refer you back to the excellent
Blumenthal-Hawley framework that has already been presented.
Chair Durbin. I was hoping you'd say that.
[Laughter.]
Mr. Harris. It contains the key elements that I would
recommend. Even if I had not read it, I would've said licensing
and registration of AI systems and the companies that make
them; liability, clearly holding AI companies liable for the
products that they make; and provenance, giving people the
ability to know what content is produced by AI and what content
is produced by humans.
Those are just a few pieces that are already in the
framework, and I'm excited to see legislative text with the
details of that.
To your question about talent, this is not the first time
this issue has come up in the Federal Government. You might
recall the healthcare.gov launch, and that was----
Chair Durbin. Oh, yes.
Mr. Harris [continuing]. That was, I believe, one of the
first major public moments when people realized it was very
hard for the Federal Government, under procurement and staffing
procedures, to hire the right people to launch large-scale
technology projects.
Now, there have been advances since then. There's something
called cyber pay that allows agencies to pay a little bit more.
But I don't think it's gone far enough.
I think there's a situation now where one of the techniques
to recruit is to find people who've already made a good amount
of money in the private sector and can make a little bit less
for a few years and then know they're going to go back.
I don't actually think that's a long-term sustainable
model. I think the rates of pay still need to go up.
But I would like to call your attention to the AI Safety
Institute, which exists within this--within the Department of
Commerce, that has actually started hiring up for this task at
hand. I think that there are incredible people there that need
a lot more money. And I hope that you will give that to them.
Chair Durbin. Thank you.
Chair Blumenthal. Thanks, Senator Durbin.
Thank you for your support, and the others, your support
for the framework that Senator Hawley and I have developed. I'm
not going to hold you to support for the bill because the text
is important.
We hope it will be available very, very shortly. But your
support for the principles and the basic framework is very,
very important to us and encouraging us to go forward.
I'm going to call on Senator Kennedy, and then he'll be
followed by Senator Padilla. We have a vote ongoing. I'm going
to run to vote so that I will be back and then leave Senator
Hawley to chair, and he'll be--and we'll be tag teaming.
But we'll go forward with the questioning while I'm gone.
Senator Kennedy.
Senator Kennedy. Thank you, Mr. Chairman.
Mr. Harris, you mentioned as one of the tenets of
regulation provenance, by that, do you mean notice that when
consumers deal with the right--robot, they should be told if
it's a robot?
Mr. Harris. Thank you so much for your question----
Senator Kennedy. You don't need to thank me. You can just
answer.
[Laughter.]
Mr. Harris. There are multiple elements of provenance
technologies. One is disclosure. And yes, that is saying that
if a company uses AI and has AI interacting with you, that it
should disclose that you are interacting with an AI system or a
bot.
But another element of----
Senator Kennedy. So that's number one, is notice. If I'm
interfacing with a robot----
Mr. Harris. Yes.
Senator Kennedy [continuing]. Or with artificial
intelligence, the owner of that artificial intelligence should
tell me, as a consumer, ``I'm a robot.''
Mr. Harris. Yes, absolutely.
Senator Kennedy. Okay. That's number one. Tell me what
number two is.
Mr. Harris. So number two is sometimes referred to as
watermarking. I know that this Committee heard, in April,
testimony about this topic of watermarking.
And watermarking can happen in one of two ways. It can be
either a visible or direct disclosure that content, for
example, an AI-generated image, an AI-generated audio file
video or even text, can be a direct disclosure that it's AI
generated. You've seen watermarks that have----
Senator Kennedy. I get it. So that's another form of
notice.
Mr. Harris. Yes, you could call it that. So that's, direct
watermarking. Then there's also another technique, which is a
more indirect disclosure, which is hiding an invisible signal
within the text of a piece of text that's generated by AI
within an image, which is a hidden pattern of pixels.
Senator Kennedy. What good does that do, in terms of a
consumer?
Mr. Harris. Well, the good thing about that is that it can
be much more difficult to remove than simply a notice that says
this was produced by AI at the top or bottom of a picture that
you could just crop out and remove very easily. So watermarks
have value in that sense.
There's also another technology that was also discussed in
April here, which is called ``digital fingerprinting.'' This
technology has been used to keep track of child sexual abuse
material and terrorist content circulating online. And that
creates what's called a ``hash,'' a unique identifier for
images.
Audio files could be even done with text or videos, and
that's stored in a database, and that can associate a piece of
content and then identify it as AI generated.
Senator Kennedy. All these are forms of notice.
Mr. Harris. I would--some more direct than others. I would
call them forms of----
Senator Kennedy. I'm not trying to trick you. I'm trying to
understand. Are there any companies that are giving the world
proper notice right now?
Mr. Harris. Um----
Senator Kennedy. Anybody?
Mr. Harris. So, I'm happy to answer that.
Senator Kennedy. Sure.
Mr. Harris. So, according to their public statements----
Senator Kennedy. Yes or no, please, sir. Because I got
other ground to cover.
Mr. Harris. Google DeepMind SynthID is the name of the
technology. Looks good, I haven't tested it.
Senator Kennedy. But they're trying to give notice.
Mr. Harris. Yes.
Senator Kennedy. Okay. Licensing. What good is licensing
going to do? I mean, you go to the Government and say, okay,
here, gimme a license. What good is that going to do?
Mr. Harris. Well----
Senator Kennedy. Anybody?
Mr. Harris. I'm happy to take that one also, briefly. As
in, is the case in many professions, the law, medicine, you
need a license to practice. And if you violate the code of that
profession, you can no longer practice. And with technology, I
see no reason why it should be different.
Senator Kennedy. So with the license, we should have a code
of behavior?
Mr. Harris. Yes, absolutely.
Senator Kennedy. Okay. And what should be in that code of
behavior?
Mr. Harris. Again, happy to offer this to any of my--my co-
panelists here.
But I believe that ethics are critical. That artificial
intelligence systems should be designed in ways that they can't
harm human beings. That they don't do damage to people. That
they don't discriminate. That they don't give people advice
that causes, brings harm to them, that's incorrect. Those are a
few elements of the code.
Senator Kennedy. So basically, I'm not--I'm trying to
understand this.
Mr. Harris. Yes.
Senator Kennedy. Because I think you're all extraordinarily
bright--that's obvious. But the American people don't
understand what you're talking about. Okay?
And, frankly, many times, neither do I. So, you license
with a code of behavior that would require Government to set up
some sort of agency to enforce that code of behavior. Is that
correct?
Mr. Harris. This is an area where there are actually
multiple interesting proposals.
There's a law professor at Fordham Law School named
Chinmayi Sharma, who has been writing about creating a
malpractice regime for engineers who develop AI----
Senator Kennedy. Well, that's what I'm getting to next.
Liability. It is possible for the private sector to enforce
through liability. Why are our tort laws right now, or our
contract laws right now not adequate?
Mr. Harris. Again--oh, go ahead.
Ms. Toner. I can jump in on that.
Senator Kennedy. Yes.
Ms. Toner. At present, it's very unclear how AI should be
thought of and how different actors, in what gets called ``the
value chain,'' should take responsibility for unintended
outcomes.
The best--best comparison that people turn to is issues
with software, issues with cybersecurity, which I think many
tort experts, which I am not one, considered to be an area of
liability that has not been especially successful in allocating
responsibility for harms. And so, you know, that's not an
especially promising precedent, necessarily.
And also, AI is very different from the kinds of software
we're used to dealing with, and the problems and harms that AI
can cause are different from cybersecurity issues.
So I think, you know, liability is a way, if there can be
clear allocation of liability, that's a way of, sort of,
flexibly setting incentives that depend on the specifics of any
given case, that could potentially be quite helpful if it were
possible to provide more clarity.
Senator Kennedy. Thank you.
Senator Hawley [presiding]. Senator Klobuchar.
Senator Klobuchar. Thank you very much, Senator Hawley.
I think I'll start, given that Senator Kennedy was just
asking questions, he and I have a bill on journalism
compensation. I'd ask this of you, Dr. Mitchell. We want to
make sure that this is fair for people that are producing
content and that they're getting paid.
And one concern is that AI developers may train their
models on content from journalists and other content creators
only to regurgitate that content without attribution or
compensation. And that's why last week I sent a letter on this.
And in your testimony, you say that AI firms should conduct
due diligence on the foreseeable outcomes of a technology
before it is deployed. Obviously, Senator Kennedy and I and
others believe that we should have some kind of agreement on
what compensation is here.
But, could you talk about harms that new AI features pose
in markets like journalism, which is key to the first amendment
that we have functioning journalism? Go ahead.
Dr. Mitchell. Yes, thank you. And that's such an important
point. So there's a couple things here. One is that, machine
learning models that are trained on previous news articles are
only able to speak to the past. Right? So, you know, one of the
critical needs in journalism is to continually be up to date
about what's actually happening.
And we lose that when we start to just rely on, sort of,
past statements and regurgitated in a way that may be relevant
for a current situation. So that's one, sort of, key issue.
Another issue is that, generative AI technology seems to be
somewhat replacing journalist jobs, at least I personally know
lots of journalists who are now out of the job, and they have,
you know, managers who are pushing use of this technology more
and more.
That's problematic because, you know, the biases of the
system that's generating the content is going to then
proliferate throughout the different articles that are being
written. When ideally, we could have lots of diverse
perspectives reporting on different material.
And then it also, sort of, waters down or makes a little
bit more bland the information that's put out there and
available. And there's also this additional problem of
automation bias, where even if you're working with generated
content, you might be more likely to accept it because it's
been generated by an automated system, where it might not be
saying something that you otherwise would be saying.
So there are a lot of issues here where I think
journalists, in particular, should be protected, relevant to
language generation. And minimally, have compensation when
their work is included in the data.
Senator Klobuchar. Okay, thank you. We obviously have a
number of bills, people are working on this Committee,
including Coons, and Tillis, and Blackburn, and I, on people's
right to their own images, and the like.
But I thought I'd focus just on one thing today, and I have
a number of bills with Senator Thune, and others, to make sure
we have regulations at work and guardrails in place, just
because I see the potential upside of this, as well.
And I think the only way we're going to get there is if we
don't have a bunch of people ripped off in scams, and if we
don't have a harm done to our democracy.
So, two ways that we look at this. One, is to do--we have
two bills that are major, one is with Senator Hawley and myself
on banning the deepfakes where it is actually someone
pretending to be the candidate when they're not. We've seen
this happen on both sides of the aisle. And we've seen other
States, including Texas, actually take action on this.
We have, I think 18, 19 States have done this, including
red States, blue States, purple States, where they have, at
least, required labeling of ads.
And we understand in our bill, Senator Hawley, and myself,
that you're not going to be able to cover satire and that kind
of thing, because of the Constitution.
But the labeling requirements, that Senator Murkowski and I
have, we'd get at not small-ball stuff, like hair color, but
we'd get it, at least, so that people know--if it is the
candidate or not--themselves.
Because even with satire, I know people are confused
because they've showed me stuff and said, is this really them?
Is that really Trump? Is that really Harris? Is that really--I
mean, it's an unbelievable thing to do to voters when you're
trying to get them to make a decision.
So I just look at this as two-pronged, and I guess I'd go
with you, Mr. Harris, because you talked about this earlier. Do
you agree there's a serious risk posed by the use of AI in our
elections? And can you tell us how AI has the potential to
turbocharge your election-related disinformation?
Mr. Harris. Thank you so much, Senator Klobuchar, for the
question and for your commendable efforts on legislation
producing all of the bills that you mentioned.
Senator Klobuchar. We have gotten those two bills through
the Rules Committee, but Senator McConnell is having an issue
with them. And I'm just really concerned that we are not going
to be able to advance them on the floor before it's too late--
but continue on.
Mr. Harris. Yes, absolutely. So this is a topic that I have
written about extensively, in part with--in my work with the
Brennan Center for Justice at the New York University School of
Law. I produced a guide for how election officials can prepare
for AI threats along with co-authors. And I think that it's
very, very important that we be prepared for a variety of
threats.
Those threats could include deepfakes of election
officials, of candidates like yourselves, and also deepfakes
that present election apparatus in them that indicate that
there was tampering with physical objects associated with the
election.
In two bills, that I mentioned earlier, that I worked on in
California that are currently awaiting signature--fingers
crossed--by Governor Newsom: Assembly Bill 2655 by Marc Berman,
Assembly Bill 2839 by Gail Pellerin--those would actually have
serious consequences so that if someone posted those types of
election deepfakes, they could be removed. That platforms would
be required to remove them.
Senator Klobuchar. Yes, well, our bill allows for them--
many of them are actually supporting this bill because it makes
it clear the platforms are that they would have to take it
down. But it also puts liability on the people that put those
up.
Mr. Harris. Yes.
Senator Klobuchar. Potential liability, which is a way we
handle so many areas, liability for whatever malfeasant group
decided to fake that it's the candidate.
And I just--I--anyway, I hope that's their direction. I
haven't seen those bills. I just know what they've done in
other States, including, as I said, Texas, Mississippi, and
many others.
So I'm--I'm just sad and very concerned that our Federal
Government isn't doing anything, and moving these bills.
Because we don't have anything really that protects Federal
elections, which is kind of a big deal.
We'll try to use existing laws, but the State laws would
remain in our bills. But it would just simply give us the
protection of Federal elections.
So, I guess that the--Mitch McConnell has decided we're
going to just roll the dice and see if it all is fine. But I'm
already having people come up to me and say, is this really the
person? I don't think this is a person. And we could stop this
right now by putting these bills in place.
Senator Hawley. Senator Padilla
Senator Padilla. Thank you. Thank you, to all of you, for
your testimony today.
As you're, I'm sure, aware, the U.S. AI Safety Institute
recently announced a deal with OpenAI and Anthropic who agreed
to share their models with researchers before and after
deployment for research, for testing, and for evaluation. My
first question is for Ms. Toner and for Dr. Mitchell. What do
you think about this agreement?
Ms. Toner. Thank you, Senator Padilla. I haven't seen the
details of the agreement. I think, in principle, it sounds
excellent. I think it's a great step forward. I'm excited by
the work that the AI Safety Institute is setting out to do, and
I echo Mr. Harris' call for them to be as well-resourced as
they possibly can be.
I think, you know, the success of an arrangement like this
will depend on a lot of details about timing and access and
what kind of assets are allowed to be accessed in what kinds of
ways. So I'm optimistic about it and very pleased to see the
agreement. But, you know, we'll have to see where it goes from
here.
Senator Padilla. Dr. Mitchell.
Dr. Mitchell. Thank you. Yes, I echo Ms. Toner. I think
that one of the reasons why this is really critical is that it
can help keep companies, sort of, accountable for the
statements they make and the kind of things that they might be
misleading the public about with respect to how well the
technology works, when you can have kind of independent
examinations or reproducibility of the evaluation results or
more rigorous evaluation, this kind of thing.
I think we're in a better situation for developing AI in a
way that's very well informed.
My one concern and, you know, again, this is not knowing a
lot about this particular agreement, would be if research was
abused. So, for example, saying you're a researcher and then
using the technology in a malicious way, I think this happened
previously with Facebook.
And so, you know, there's a lot of detail there about how
you decide that there's a researcher that is okay to be using
it. And, you know, there's a lot of potential issues there. But
at a high level, I do agree that this is an incredible thing to
do, and we need to have in general, sort of, these independent
analyses that are able to hold tech in check.
Senator Padilla. So just as a follow-up, I think you both
began to touch on it. How would you advise policymakers to view
what success looks like from these agreements and arrangements?
Let's start with Dr. Mitchell, then come back.
Dr. Mitchell. Yes. So one thing, in particular, that I've
been really interested in is just evaluation--and rigorous
evaluation in how you really quantify how well these systems
work. I think that larger tech companies are, sort of,
incentivized to report that the systems work well without
really breaking down what that means, the context of use, when
it can be used, when it can't.
And one thing that, sort of, independent research might be
able to do is actually do pretty rigorous analysis of how the
systems might work in specific kinds of scenarios and provide
just a lot more insight that would not be provided if, you
know, they had more of a profit incentive.
Senator Padilla. Ms. Toner.
Ms. Toner. Yes, just to add onto that, all of which I agree
with. I think it'll be difficult for us from the outside to be
able to evaluate, I believe, because I expect a lot of this
testing and evaluation to happen behind closed doors, which I
think is reasonable.
And so, I think the task for Congress will be to be
interfacing and working closely with NIST and the AI Safety
Institute to hear from them how well this is working.
Do they have, again, the sort of contractual arrangements
that they need in order to carry out the kinds of testing that
they think would be most valuable? Do they have the funding
that they need? Do they have the staffing that they need? And
so, we've talked a little bit about the need for more technical
talent in Government.
Personally, I think salaries are only one piece of the
puzzle there. I know multiple people who are really interested
in offering their immense talents to the U.S. Government and
are just--it's the processes, the hiring processes are
impossible.
And another element that I know comes up for people who do
end up getting into Government is, you know, their ability to
use halfway-up-to-date technologies rather than extremely old
devices, extremely old systems.
So I do think that the talent issues are critical, and I
think that the salaries are part of that. But I worry sometimes
that we see the salaries as a totally intractable problem and
then give up. And I think there are other ways to increase
access to technical talent, as well.
Senator Padilla. Okay. Thank you.
And in my time remaining, I do want to touch on one other
topic, and this is for you, Dr. Mitchell. In your testimony,
you observed that there's currently no well-developed science
that analyzes how inputs affect outputs once a model is
trained. In simple terms, can you help all of us better
understand why this is important for developers to consider?
Dr. Mitchell. Sure, yes. So, an analogy to consider might
be with baking or cooking. So, when you make a cake, ideally
you have a sense of what the ingredients are and you know what
the difference ingredients might do. So if I add more egg,
it'll be more puffy, you know, these kinds of things. We don't
have a similar thing with building models.
And so, if you think of the data as essentially
ingredients, and the training is cooking, and then the model
is, sort of, the output, the thing that you've cooked, we're
missing the, sort of, approach where we have recipes. We're
missing this, like, deep understanding of what all the pieces
are that result in this output thing we might want to eat or
not.
Senator Padilla. So, we've gone from the animal kingdom----
[Laughter.]
Senator Padilla [continuing]. To baking, but in a very
effective way. Thank you, all. Thank you, Mr. Chair.
[Laughter.]
Chair Blumenthal. Thank you very much, Senator Padilla.
All of the witnesses that we have before us today have left
companies, AI companies, based on concerns about commitment to
safety.
You're not alone, obviously. OpenAI, in particular, has
experienced a number of high-profile departures, including the
head of its Superalignment Team, Jan Leike, who left to join
rival Anthropic.
And upon departing, he wrote on X, quote, ``I have been
disagreeing with OpenAI leadership about the company's core
priorities for some time until we finally reached a breaking
point.'' He also wrote that he, quote, ``believes much more of
our bandwidth should be spent getting ready for the next
generation of models on security monitoring, preparedness,
safety, adversarial robustness, superalignment,
confidentiality, social impact, and related topics. These
problems are quite hard to get right, and I'm concerned that we
aren't on a trajectory to get there.''
Let me ask all of you, based on your firsthand experiences,
would you agree essentially with those points? Let me begin
with you, Mr. Saunders, and go to the others if you have
responses.
Mr. Saunders. Yes, thank you, Senator. Dr. Jan Leike was my
manager for a lot of my time at OpenAI. And, you know, I really
respected his opinions and judgment.
And I think, you know, what he was talking about were, sort
of, a number of issues where OpenAI is not ready to deal with
models that, you know, have some significant catastrophic
risks, such as high-risk models under the preparedness
framework.
So, things that could actually, like, start to, you know,
assist novices in, like, creating biological weapons, or, like,
you know, the systems that could start conducting unprecedented
kinds of cyber attacks.
And so, for those kinds of systems, first we're going to
need to nail security, so that we make sure that those systems
aren't stolen before we figure out what they can do and used by
people to cause harm.
Then we're going to need to figure out, how do you
actually, like, deploy some system that under some
circumstances will, you know, could help someone construct a
biological weapon. But lots of people want to use it for
other--for a bunch of other things that are good.
So, every AI system today is vulnerable to something called
``jailbreaking,'' where people can come up with some way to,
you know, convince the system to provide advice and assistance
on anything they want, no matter what the companies, like, you
know, have tried to do so far.
And so, we're going to need to have solutions to hard
problems like these. And then we're going to need to, you know,
have a--have some way to deal with, you know, models that,
again, might be, you know, smarter than people supervising them
and might, you know, start to, like, autonomously cause certain
kinds of risks.
And so, I think yes, I think he was speaking to, you know,
again, just, like, a number of areas where the company was not
being rigorous with the systems that we currently have, which,
you know, again, can maybe, can amplify some kinds of problems.
But once we reach the point where, like, you know,
catastrophic risk is possible, we're really going to need to
have our shit together. Um----
Chair Blumenthal. Others have comments in response? Ms.
Mitchell.
Dr. Mitchell. Yes, I just agree with Jan's statement there,
and I think this also echoes some of what Ms. Toner had said
about responsible AI-type people being disempowered in tech
companies.
And so, while on the one hand it's helpful for tech
companies to have responsible AI trust and safety teams so that
they can tell Senators that they have them.
On the other hand, when it comes to making critical
decisions about how the technology gets developed, they're
usually left out of the room. So, this is--this is a serious
issue.
Chair Blumenthal. I'm going to interrupt my second round to
yield to Senator Blackburn, who has rejoined us.
Senator Blackburn. Thank you so much. Yes, there are so
many hearings today. It is catching us running back and forth.
Mr. Saunders, to the point you were just making, one of the
things we've said repeatedly is we have to have an online
privacy bill that is federally preemptive, before we start down
the AI path. Because people want to be able to firewall their
information and keep it out of the public domain.
And last week, and, Ms. Toner, I want to come to you on
this because Meta announced it was going to use public content
and things from Facebook and Instagram in the UK, and they were
going to use this to train their generative AI models, again.
And Meta said their goal was to reflect, let's see, British
culture, history and idiom, and that UK companies and
institutions will be able to utilize the latest technology. And
I think we all know what that means.
I'd love to hear what concerns you have over that
announcement and what limits should we place on companies, like
Meta, that are using this data that is shared on their
platforms to train their generative AI models.
Ms. Toner. Thank you, Senator Blackburn. My understanding
of that announcement, which I'm only, I should admit, have seen
briefly but not dug into in depth, is that that was actually a
practice that Meta was already very much, you know, going ahead
with in the United States, due to the lack of privacy
protections here. And that the announcement last week was, I
think they had----
Senator Blackburn. Probably need that privacy bill
Ms. Toner. Indeed. I think, I believe, and other witnesses
may know better, I believe that they had held off on initiating
that process in the UK, due to privacy protections that do
exist in the UK. So, to me, this is actually an example of,
perhaps, success on the UK's part.
If Meta felt the need to be a little more thoughtful, a
little more deliberate, a little more selective about the ways
in which they were using British users' data because of the
legal protections that existed there, which as you rightly
point out, do not exist in the United States. I don't know if
others want to add to that.
Mr. Harris. I'm happy to add on that.
There was actually something on this same topic that really
got a lot of attention at the beginning of the summer. A lot of
users of Facebook and Instagram found that they could opt out
of the process of having their public data used for training of
AI systems, and then they posted instructions.
I saw this on both TikTok and on LinkedIn--users had posted
instructions how to go to the part of your Facebook or
Instagram settings where you can opt out of having your data
used.
I tried to do it in Facebook, I tried to do it in
Instagram, and I couldn't find the button. And then I posted
and said, I can't find the button, in the comments there.
And it turned out everyone with their IP address in the
United States couldn't find the button because this was a
feature that, I believe, was only offered to people in the EU,
and, perhaps, in the UK--I heard different stories about which
parts of the world.
But this idea that we, Americans, are, you know, second-
class citizens, that we don't even have the right to object--
that Europeans or people in the UK have to object to--are
photos of ourselves, of our families, of our children being
used to train AI systems. And, at that, AI systems that we
don't even have confidence in how they work.
We don't know if they will accidentally release personal
information about us in the future, make images that look just
like us.
So, I applaud you for raising this issue, and I'm excited
to see bills like APRA make progress so that we have the
foundations of a legal system that can address that issue.
Senator Blackburn. Well, we think having an online
privacy--federally preemptive privacy bill that gets signed
into law is something that is going to be necessary.
And, as Senator Blumenthal said in his opening remarks, and
I'm paraphrasing them now, but basically, Big Tech has proven
to us they are not going to take steps that are necessary to
protect the information of people.
I do want to ask you something else, in, when we're talking
about AVs that act in the world without humans and they're
using AI, and that brings about the difference between
intelligence and agency, between systems that think and systems
that can act.
So, Ms. Toner, let me come to you on this. When you look at
this difference between intel and agency, do you see these as
different concepts? Do they carry different threats? Should
these be approached separately? Differently? Does this play
into AGI? Tell me your thoughts on that.
Ms. Toner. Thank you, Senator. It's a good question. It's a
timely question. We actually have a paper coming out
introducing exactly this issue for policymakers----
Senator Blackburn. Oh, great.
Ms. Toner [continuing]. In a couple of weeks.
Senator Blackburn. Good.
Ms. Toner. And what I would say is, I think these ideas of
agency or agents that can take actions autonomously is not at
all separate.
It is, you know, not the same thing as intelligence. But we
are already seeing the ability of companies to take, you know,
language models, chatbots, like ChatGPT and others, and, you
know, add a little bit of additional software, add a little bit
of additional development time, and convert them into systems
that can go out and take autonomous action.
Right now, the state of these systems is pretty basic. But
certainly talking to researchers and engineers in the space,
they're very optimistic. They're very excited about the
prospects of this category of system.
And it's something that is very actively under development,
as far as I'm aware, at all of the top AI companies, with the
goal being, you know, initially perhaps something like a
personal assistant that could help you book a flight or
schedule a meeting.
But ultimately, you know, Mustafa Suleyman, formerly at
Google, now at Microsoft, has talked about, for example, could
you have an AI that you give it--I forget the numbers,
something like a hundred thousand dollars and it comes back to
you a little while later with a million dollars that it's made,
because it's run some business or done something more
sophisticated?
At the limit, certainly, this is very--very related to
ideas around AGI and advanced AI more generally. Sort of the
founding idea or founding excitement of the field of AI, I
think, for many people has been the idea of systems that can
take very complicated actions, pursue very complicated goals in
the real world. And I see a lot of, again, excitement in the
field that we might be on the path in that direction.
Senator Blackburn. Now, in Tennessee, with our healthcare
industry, our logistics industry, our advanced manufacturing,
we see great promise.
With my entertainers and singers and songwriters and
musicians and authors, we don't want this to become a way to
steal their name, image, likeness, voice. And that's why we
have the NO FAKES Act.
And I know Senator Klobuchar, who has joined me on that
bill, talked with you all about that later. I've run way over
and you've been generous, Mr. Chairman. Thank you.
Chair Blumenthal. Thank you very much, Senator Blackburn.
A number of you have mentioned whistleblowers and the need
for protecting them. Maybe anyone who would like, could expand
on that point?
You are all insiders who have left companies or
disassociated yourself with them in one way or another. And I'd
be interested in your thoughts. Mr. Saunders?
Mr. Saunders. Yes. Thank you, Senator. So when I resigned
from OpenAI, I, sort of, found that, you know, they gave to
every departing employee a, like, restrictive non-disparagement
agreement. And you would lose, you know, all of the equity you
had in the company if you didn't sign this agreement where you
had to effectively not criticize the company and not tell
anybody that you'd signed this agreement.
And, you know, I think this really opened my eyes to the
kinds of legal situation that, you know, employees face if they
want to talk about problems at the company. And I think, you
know, there's a number of important things that, like,
employees want in this situation.
So there's like, knowing who you can talk--knowing who you
can talk to, it's very unclear, sort of, like, what parts of
the Government would, you know, be--have expertise in, you
know, specific kinds of issues you want to know. And you want
to know that you're going to talk to somebody who understands,
you know, the issues that you have, and, you know, that has
some ability to act on them.
And then you also want, you know, legal protections. And
this is where I think it's important to define protections
that, you know, don't just apply when there's a, you know,
suspected violation of law, but when there's a, you know, sort
of, suspected, like, harm or risk imposed on society. And, you
know, so that's why I think--yes, legislation needs to include,
you know, yes, establishing, you know, whistleblower points of
contact and these protections.
Chair Blumenthal. Other thoughts, Ms. Mitchell?
Dr. Mitchell. Yes, thank you. So, I think Mr. Saunders is
making one of the really important points here is that there
isn't a lot of knowledge about when and how to whistleblow. So
as part of my, sort of, ethics studies, I tried to familiarize
myself with a situation where you would whistleblow versus
whether this would be like breaking your NDA, that sort of
thing.
This is something that I had to learn myself, and ideally,
I would've had some sort of resource. Ideally there's, you
know, some agency you could call and say, hey, theoretically,
if I think there's an issue now what do I do? Right? But
essentially, if you're considering whistleblowing, it's you and
you alone against, you know, a company making a ton of money
with lawyers who are set up to harm you if you make the
smallest move incorrectly, whatever it is.
And so, I think that it needs to be very clear to people
working internally when and how to whistleblow. And it needs to
be very clear at the highest levels of the company that that is
supported. I could even imagine having orientations where
you're required to provide information on whistleblowing, that
kind of thing. But currently there's no information internally.
And you're very much on your own in a situation where you might
lose your job and then not have the money to pay for a lawyer
to fight it.
Chair Blumenthal. Ms. Toner.
Ms. Toner. Thank you. I just--to put a finer point on
something that I think both Mr. Saunders and Dr. Mitchell are
describing, I think a core to the problem here is that the lack
of regulation on tech means that many of these concerning
practices are not illegal.
And so existing whistleblower protections, it's very
unclear if they apply at all.
And if you're a whistleblower--a potential whistleblower
sitting inside one of these companies, you don't really want to
go out on a limb and take a guess at, well, is this enough of a
financial issue that the SEC would be, you know, would cover
me? Or do I have to go talk--if it's something that's kind of
novel related to AI development or other technology development
where you have serious concerns, but there's not a clear
statute on the book saying the company is breaking the law,
then your options are limited.
So, I think the need for whistleblower protections goes
hand in hand with the lack of other rules.
Chair Blumenthal. So that--I think the point that you've
just made is really important that the failure to develop
safety and control features in a product is not illegal,
perhaps, and therefore may not be covered by a strict reading
of whistleblower laws, even if it is a practice which is
unethical and harmful. I think that's a very important point.
Let me, sort of, go to the other side of that question,
which is the incentives, whether promotion or compensation,
bonuses and other kinds of incentives that are offered by the
company.
Do they align with safety, for example, are employees
rewarded for developing better safety features? Or, is it more
rewards for racing to the market, which is the dynamic that
we've discussed today? Mr. Harris and--or Mr. Saunders? Mr.
Harris.
Mr. Harris. Yes, I could share an anecdote about that,
Senator Blumenthal.
Early in my tenure at Facebook, I was introduced to someone
who was a friend of a friend, and I told them that I had joined
the Civic Integrity Team, and this person said, oh, that work
sounds so interesting.
But, you know, I just never joined teams like that because
you can never show impact. You can never--you never have
anything to say on your performance reviews about what you did,
because the only thing you achieved was hopefully to not have
something happen.
And I heard different versions of that throughout my time
in the tech industry, not just from inside of the companies
where I worked. But I heard that across the board. It's very
hard to make progress in your career when you work in an area
that doesn't have clear ways to show progress.
I also heard of people being asked whether they could
demonstrate, that work that they did reduced the number of
public relations emergencies for the company.
How can you possibly demonstrate that you reduced the
number of public relations emergencies for a company? So,
again, these are structural problems with doing that work. And
I applaud you for bringing up that question.
Chair Blumenthal. Thank you. Mr. Saunders?
Mr. Saunders. So, I think there were, you know, a couple
of, like, significant, like--I don't know, like, goals that,
like, OpenAI as an organization has, you know. So, one of these
is, like, called, like, maintaining research velocity. And so,
you know, on things like security, OpenAI is reluctant to do
things that will, you know, require additional work from
researchers or might slow them down to, like, you know, use a
more secure system.
And then, you know, on more of the, like, testing side,
there's, like, often, like, you know, release dates that are
set where, like, the company starts, you know, talking to a
bunch of, you know, customers and saying, like, oh, yes, we're
going to ship by this date.
And then, sort of, like, the amount of safety work is, you
know, determined after, like, you know, picking off, like, how
quickly you want to ship, you know, and subject to, like, you
know, office politics and these kinds of things.
And so, like, this is because companies just face, you
know, an enormous incentive to be, you know, seen as the
company that's, like, leading in the AI space.
And so, this is why we need to have, you know, some kinds--
there needs to be some kind of, you know, regulation to be,
like, doing the right thing, because otherwise it's going
against the grain.
Chair Blumenthal. I'll give Ms. Toner and Ms. Mitchell a
chance to respond, as well, if you have any responses.
Dr. Mitchell. Yes, thank you. I echo my co-witnesses. We
used to, sort of, I've previously, sort of, realized that it's
difficult to get promoted if you focus on safety, this kind of
thing, because you're not promoted for the bad headlines that
never exist. Right? And so, you're trying to prove something
that never actually happened.
And this also speaks to the role of foresight and why
foresight isn't incentivized. Because if you take these extra
steps to make sure that something bad doesn't happen, you can't
prove that it could have happened. So there's--there's really--
it's really quite difficult to focus on safety and ethics and
these kinds of things.
In general, your promotional velocity is much less compared
to your peers, which also means that you are less likely to
become a leader at the company in order to further set norms.
So, by focusing on things like safety and ethics, it's a
way to, sort of, remain at the lower levels of a company and
not be able to fundamentally shape it for the better.
Chair Blumenthal. You know, we've seen examples already of
our foreign adversaries using AI to meddle in our democracy.
I think last month OpenAI revealed that the Iranian
government-linked group had used ChatGPT to create content for
social media and blogs attempting to sow division and push
Iran's agenda in the United States.
There have been reports that China and Russia have also
used AI tools deceptively to interfere in our democracy. I
mentioned them earlier. I think the threat to our elections is
real and we're unprepared.
Mr. Harris, you worked on a California law that seeks to
safeguard our democracy. Are those the kinds of protections
that you think would be effective at the Federal level? And is
there more that you would add to the California law?
Mr. Harris. Thank you so much for the question, Senator. I
believe that in California, it's difficult to make a lot of
types of laws that could be made at the Federal level.
There are a number of reasons for that. One is simply the
State agency infrastructure is dramatically smaller than the
Federal agency infrastructure.
And in California, in a situation of budget deficit it's
very hard right now to pass any legislation that has any
significant cost. That, I believe, is one of the biggest
barriers to passing the legislation that we need.
And I think that you have in front of you, in your
framework, the type of legislation that California would not be
able to achieve, things like licensing and registration and
liability. Those would be very costly at the scale of a State
to enforce those.
And to be honest--it would of all the States in the country
maybe be one that cost California more than many others, simply
because of the location of the technology industry there. And
there might be political conditions that make it harder in
California to pass that type of legislation.
Chair Blumenthal. Again, I'll--I'll entertain any points
about the California law that any of you would like to make.
And if not, let me follow up, Mr. Harris, in your
experience there, what was your--what was your takeaway from
the tech companies? Were they supportive? Helpful? How would
you characterize their reaction?
Mr. Harris. Thank you so much for the question. I believe
that you need to look at two different phenomena. One is the
outward presentation of the tech companies about legislation.
And then another is, what's happening behind closed doors.
I made a reference in my opening statement to the idea of
``shalls'' and ``mays.'' I was surprised.
I have been surprised in my work in the California
Legislature by the way in which tech industry lobbyists,
sometimes hiding behind industry groups, sometimes from
individual named companies, are able to arrive at legislators'
doors with requests to remove ``shalls'' and replace them with
``mays.''
To take legislative language that was very well
intentioned, and at the 11th hour, turn it into something that
is meaningless.
It concerns me greatly, what I've seen in California. There
are political realities that, if draft legislation comes from a
civil society group, like the one that I work--that I have been
working for, the California Initiative for Technology and
Democracy, that if those pieces of legislation are too bold,
sponsors of that legislation, organizational sponsors, will be
told this isn't going to work and you're going to have to
weaken it.
And sometimes that comes in many rounds of weakening, and
it can be very painful to watch.
Chair Blumenthal. There are two studies--you mentioned a
study, Mr. Harris, which we will put in the record along with
your testimony.
[The information appears as a submission for the record.]
Chair Blumenthal. I want to mention two others. A Stanford
Internet Observatory Report this past December that found that,
that training data sets used by AI companies are filled with
thousands of known images of child sexual abuse.
Possessing that kind of material is a crime.
And as you wrote, Dr. Mitchell, in your testimony, training
AI with these kinds of abusive materials means there's a good
chance that the model will generate new abuse material.
The second study is a survey released last month by the
anti-exploitation group, Thorn, one in 10 preteens and teens
reported that their friends or classmates had used AI tools to
generate sexual images of other kids--one in 10 preteens and
teens saying they'd use these tools to generate sexual images
of their peers.
So, these new AI tools, which are trained on child sexual
exploitation, are fostering sexual harassment of young people,
and the apps to create that abuse material are easy, they're
free to find on Google and Apple App stores right now.
Possessing and creating child abuse material, clear example
of the problems with this race to the market, which can easily
turn into a race to the bottom.
There must be steps that generative AI companies can be
taken to make sure their tools aren't being trained on illegal
child exploitation material, and aren't creating these kinds of
abusive images of anyone, let alone kids. Would you agree? And
what kinds of steps should these companies be taking?
Dr. Mitchell. Yes, thanks for the question. So, this speaks
to a few different issues that I think I also hit on in my
written testimony. So, one is just how data analysis is really
not a norm within AI culture, generally. And so, we don't
really understand when we're training a model, what might be in
the data.
And part of my work has actually tried to invent methods in
order to probe data sets, in order to figure out possible
problematic content, and, you know, it falls on deaf ears.
There really isn't an interest in it.
So, I think that work that can further incentivize this, I
think NIST and the U.S. AI Safety Institute probably have a
very clear role to play here in helping us understand what the
contents of the data are, and further providing mechanisms for
people to actually do this in a lawful way in order to really
understand the contents.
I'll also add, this is just, sort of, a--sort of an insider
knowledge that might be useful for you to know. A lot of the
data that ends up being used in machine learning systems are
collected via an organization called Common Crawl.
I do wonder if there might be room for the Government to
help Common Crawl to analyze the data before it's released at
all, which would stop the further proliferation right where it
starts.
And the last part I want to make is this is also where
watermarking and provenance information is really key,
including invisible watermarking, which is open research. But
something that would be very, very helpful in order to trace
back where this came from.
Mr. Harris. If I could add, Senator Blumenthal?
Chair Blumenthal. Absolutely.
Mr. Harris. So, yes, I agree with the statements that you
made. And I think there are a few lessons that we should learn
from this current situation.
One is, again, voluntary self-regulation does not work.
There is another public statement about this topic that was
signed by a number of companies that signed onto what, what
Thorn and All Tech is Human, another tech nonprofit called the
Safety by Design, principles.
And amongst the signers to that included Stability AI, I
believe Hugging Face, and other company called CIVICAi--and
they signed this and made agreements about doing things to
prevent the use of child sexual abuse material in AI models.
Now the problem is that, that report that you referred to
from the Stanford Internet Observatory, that was released in
December--but until just a few weeks ago, there had been no
moves to actually take down the offending AI models in any
serious way.
A few weeks ago, when I was on the eve of publishing a
paper on this topic with IEEE Spectrum, a technology
publication, I reached out to those companies and perhaps in
response to my outreach, perhaps not, one of the companies
involved RunwayML--the company that people point to as being
responsible for training Stable Diffusion 1.5, the specific
model trained on thousands of images of CSAM--they actually
removed their Hugging Face repository.
Now, the problem with removing a repository--this was an
open-source AI model. The problem with removing it is that
there were many other versions of that model that are still
hosted on Hugging Face.
And my co-panelist, Dr. Mitchell, is appearing here in her
individual capacity. And I have immense respect for her
research and do not expect her to feel the need to speak up on
behalf of or in response to my statements here.
But Hugging Face has chosen not to take down other versions
of Stable Diffusion 1.5.
Another company called CIVICAi has a website with many,
many models derived from Stable Diffusion 1.5, perhaps the
original model itself--or something close to it. They also
choose not to take it down.
And these are the open-source models that are likely behind
the report that I shared with you from Graphika, that will also
go into the record, states that a big reason for the rise of
these undressing or nudifying apps is the advancement of open-
source AI image-generation tools.
And until we can get the companies that are hosting these
tools to take just the minimum level of responsibility--I mean,
I've done content moderation before. I've worked on this issue.
If something is called Stable Diffusion 1.5, Stanford says
that was trained on child sexual abuse material, why let
someone upload something called Stable Diffusion 1.5 today?
After it was taken down, more people uploaded dozens more
versions of it.
Chair Blumenthal. Great. Any other comments on that
question? Dr. Mitchell?
Dr. Mitchell. Yes, I mean, I'd love to follow up with you,
if that's the case. I wasn't aware of that.
So that's--that's very important information for me to
know. So, yes, I mean, this is a serious concern, and I think
this is part of why we need the Government's help in order to
be able to even search for these things. Right?
So, there are techniques you can use to try and understand
if there might be an issue. And I won't detail them here
because of, you know, potential malicious actors watching, I
can follow up with you, privately.
But trying to specifically identify the content, I think
breaks several laws. So you end up in a weird sort of bind
where you have to try and see if it's there, but you can't
really see if it's there.
And then it ends up with, you know, potentially censoring
some content that really shouldn't be censored. So, it is
really, really difficult, and we could use the Government's
help on this directly.
Chair Blumenthal. I'm not suggesting that it's a simple
issue, but there is a need for action. And I think all of you
have made the point that we can't rely on the companies alone
to take action because of the incentives that they have and
that they communicate to their employees, which are, get to the
market as soon as possible, and let's worry about hindsight
afterward.
Someone mentioned open source. Is that a consideration for
us? What should we think about open source in this context of
protecting the public? Dr. Mitchell?
Dr. Mitchell. Thank you. Yes, and so, this is something
that I actually work on, you know, within my professional
capacity. So there's this term open source that I think is used
a lot without fully understanding what it means. It's also
used, I think, in situations where it doesn't necessarily
apply.
The thing to recognize is that there's a gradient of
openness that you can have things that are more open or less
open, depending on foreseeable risks. So for example, one of
the things I've been involved with at Hugging Face is
implementing gating, such that you can't access a model, you
can't access a data set, unless you've provided personal
information, unless you've taken a training and can show the
certificate, that kind of thing.
And so there's an entire spectrum from fully closed to
fully open, where I think we can do a lot of really good work,
depending on the specific foreseeable problematic uses of
different models and data sets.
Mr. Harris. If I could?
Chair Blumenthal. Sure.
Mr. Harris. So, I've written extensively on this topic of
open source, and I think there are a number of important
points.
The first one that I would like to caution you of, in
advance, is that in the European Union, in the lead up to the
signing and the finalization of the EU AI Act, a number of
companies pushed very, very hard to get full exemptions for
open-source AI from the provisions of the EU AI Act.
I would expect, if I were in your position, that you will
receive these types of requests here, if you have not already.
Exempting open-source AI from AI regulation is not
appropriate. That's because once you release an OpenAI system,
some people now, even including the Federal Government, are
using a different term, ``open weights''--that's perhaps a more
appropriate term.
The Executive order on AI uses the term, ``dual use
models,'' foundation models with widely available model
weights. There's a lot of terminology issues here. I'll go with
open weights, because I think that's the clearest one right
now. Also, ``unsecured'' is a way to think about it.
So, when you release open weights, unsecured models, you
never get them back.
If they're trained on thousands of images of CSAM and they
can produce CSAM, child sexual abuse material, you will never
retrieve that. It will live on.
You can do things to make it hide out in only the hidden
corners of the internet. You can make it less available. And
that's the case with all of these open weights or unsecured
models.
And unfortunately, we've seen a number of companies take
that strategy without doing significant safety testing on
models.
And that's why I'm very concerned about this, and I believe
that there should be no exemptions to any AI laws for open
weights, or unsecured AI systems.
Chair Blumenthal. You know, just a few questions in
closing. One of the arguments that we hear in this realm, as
well as many others, is regulation will inhibit innovation. Or,
regulation will impede our competition with our adversaries,
China being one. We've had some questions about China.
Should we be concerned about impeding innovation or putting
companies in this country at a disadvantage with efforts
abroad?
I think that's a question I'd like to ask all of you. Ms.
Toner?
Ms. Toner. Certainly, happy to begin. I think it's a
question that is worth your consideration and should certainly
factor heavily into how regulation is designed and how Congress
makes decisions about what legislation to pass.
But I think it is used as a catch-all rejection of any kind
of regulation, and I think that that is mistaken. I think it's
mistaken for a few reasons. You know, primarily--primary among
them, that there are many, many kinds of regulation, and some
of them inhibit innovation quite a lot. Certainly, poorly
designed heavy-handed regulation can do that. But plenty of
kinds of regulation do the opposite.
So, you know, one example is there were--my understanding
is in maybe the 1970s or 1980s, there were rule changes
around--maybe more recently, there were rule changes in the
financial sector around who was responsible for credit card
fraud that made the banks, you know, have to foot the bill if
cards were used fraudulently.
And one significant result of that regulation was
significant innovation in credit card security and different
mechanisms to reduce the chances of that fraud. So that's a
regulation that both protected consumers and also directly
spurred innovation.
I think a mechanism that to me seems very important for AI,
that I mentioned before and can elaborate on a little, is this
idea of consumer trust or consumer willingness to use this
technology, to rely on this technology, not just consumers,
also--also businesses, you know, enterprise use of AI.
Right now, we're in a state where people don't actually
trust this technology. They don't have the experience that it's
particularly reliable or that it necessarily, you know, has
results that fall within their expectations. And that makes
them less willing to use it, which means the companies are
earning less revenue, which means that they're less able to
reinvest that revenue in research.
So, I think there are, you know, examples in, you know,
food safety is an area we're very fortunate in this country
that, you know, we have to think almost not at all about the
safety of our food going from day to day because we have such
good food safety standards. And I think, you know, the AI is
very, very far from that--that point.
So, I think, in my mind, almost all of the policy
recommendations that have been made today are the kinds of
recommendations that really would not inhibit innovation at
all, and, in some cases, might actively promote it,
Chair Blumenthal. Mr. Saunders?
Mr. Saunders. So yes, I think this is, you know, an
important a question to consider in any regulation. And then I
think there are approaches, you know, I think the things that
are going to really harm innovation is if there is a large-
scale AI disaster, you know, in the way that, you know, like,
nuclear power was kept back on after Chernobyl. Right? That
will really lose trust in the industry.
And I think another way to, like, you know, sort of, like,
lose the race for everyone, frankly, is if we develop cutting-
edge AI systems and then they're stolen by a foreign government
before we understand, you know, that they're dangerous, and
now, like, you know, you end up at a situation where both
America and some--and another country are sort of in a standoff
where they're both afraid of each other using the systems. This
is a way that, like, we would all lose. So, I don't think, you
know, you could have a lead without, you know, being really
careful about security.
And then I think, you know, the sort of kind of frameworks
are, like--and ideas that are emerging are, sort of, tied
around regulat--you know, testing systems to figure out when
they reach levels where they have certain dangerous
capabilities.
And then, you know, adding requirements only when that
point is reached. And then, you know, if that testing is done
rigorously, like, you know, then you can, like, respond before
the horse leaves the barn.
And, like, companies are starting to agree to some sort of,
like, voluntary frameworks that have this character. But there
aren't, you know, like, laws in place that would enforce that.
And so it will be--it'll come to some, you know, very difficult
decisions where, you know, there are sort of, like, on one hand
there is some system that has, you know, maybe billions of
dollars put into it. And on other hand there are, like, you
know, unresolved safety concerns or, like, testing that is
rushed or something.
And so I do think both that we, like, need to have
standards around that. But I also do think--I also do believe
we can figure out how to make this technology safe. And, you
know, I think it's, like, the worst problems they're going to
have, is if the companies don't take the time to prepare, and
then they wait until the last minute, and, you know, it's not
clear what the--what the rules and restrictions will be. And so
then again, they end up in a situation where they really want
to ship something that really hasn't been ready.
And so, it's much better if, sort of, the rules and
guidance are laid out in advance. And companies know that if
they build something that, you know, is capable of causing
catastrophe, you know, here's what the requirements they'll
have to meet are. That's, yes, that's what I think the best
path for legislation to both protect the public and preserve
innovation.
Chair Blumenthal. Thank you. Mr. Harris?
Mr. Harris. Thank you so much, Senator, for the question.
In anticipation of this line of questioning, I actually
included a sampling of quotes from AI companies--CEOs asking
for more regulation. There are so many. It appears that almost
all of the CEOs of all the major AI companies have called for
this.
Now, I do want to caution that I see some kind of
disconnect.
I suspect that the disconnect is that the lobbyists are
not--the lobbyists that work for these tech companies are,
perhaps, not in direct communication with the CEOs, maybe
something gets lost in communication, maybe they're actually
automated themselves and they're still in an old version of
their operating systems.
But they come in with a goal to kill or hobble every piece
of legislation about AI.
Seems like it's autopilot, because I think if they thought
more deeply, talked to the CEOs who said those quotes that I've
given you in the written testimony, they would be more
thoughtful about collaborating on these efforts to regulate.
But I would also submit to you that in addition to that
evidence, CEOs are calling for it, I think if they're calling
for it, that probably doesn't mean they think it's in all cases
going to impede innovation.
Also, it's advantageous to tech companies to have clear
rules of the road upon which they are racing.
Otherwise, they're going to end up parked at the--in the
parking lot of the courthouse, depending on regulations that
stem from decades-old laws that are brought to bear on them, or
tort law, which will just lead to more cost and confusion, or
States with many different laws. And that itself is more
costly.
So, I do believe that clear regulation is advantageous to
innovation and to the tech companies, and we should take their
CEOs' word for it.
Chair Blumenthal. Dr. Mitchell?
Dr. Mitchell. Yes, thank you so much. So I think I covered
this a bit in my written testimony. But I do see a way, perhaps
naively, that regulation might actually be able to help
innovation.
So, I'll give you an example of privacy, for example. It
was previously thought that you couldn't ensure privacy with
mathematical guarantees. But with a goal set for ensuring
privacy, the research community was able to develop ways where
you could mathematically guarantee privacy.
And so I think the Government might have a role to play in
setting goals that must be demonstrated: statistical fairness,
privacy, security, safety, these kinds of things in order to
incentivize further development on how to do that in the best
way.
Again, this might be naive, I know that there's
``regulation stifles innovation.'' But I've generally seen ways
where it might be helpful.
I do see how regulation might be problematic in the case
where it tries to change the behaviors of development. So, for
example, saying you can't infer gender might mean that you
can't create a model that you can certify, does not
discriminate against gender, that kind of thing.
So, by getting into the details of how the tech is
developed, there might be serious issues. But I think by
setting high level goals of what must be demonstrated,
regulation can be very useful.
Chair Blumenthal. I think these points are very, very
important. If there's a disaster on AI, it's equivalent to a
food company taking the issue of food safety, having a major
outbreak of Listeria food poisoning. It has to recall products,
its credibility is undermined, it's a major marketing disaster.
And this kind of AI catastrophe would impact not only that
company, but possibly others, as well. So, there's a very
rational and powerful argument for regulation that makes sure
that everybody is abiding by commonsense, rational standards
that are good for the industry.
And the idea of some certainty. You know, one after
another, for 20, 30 years, businesses have told me, give us the
rules. We may disagree with you, but just tell us the rules.
And don't change them from year to year. We need some
stability. We can work with whatever rules you give us, but we
need to know what they are, and they need to be clear, and they
need to be stable.
And frankly, I take them at their word, because I think,
again, in their rational interests, that ought to be their
attitude. Unless we do something crazy.
Nobody here wants to do something crazy because we
recognize the immense good that can come from AI. And this goes
back to the point that you made, Ms. Toner, that it would be
easy to say, let's ban AI, but it has enormous potential for
good. Take the medical area, you know, the potential for
research, as well as for treatment and diagnosis--immense.
And it's a little bit like, and forgive me for being over
simplistic, but I've been doing some reading on COVID, the
origins of COVID. The debate about, did it start in a lab? Was
there a release, was there research that tried to develop a
more potent evil virus, or did it come from the wild?
We, at this point, we don't know, at least, there are
equally--or I shouldn't say equally, there are theories that
have credibility that argue for all of it. But we do know that
there are dangers in research on gain of function, trying to
develop more lethal viruses.
Now, the argument for that research is, then we'll know how
to deal with them. The argument against it is, why would you
develop a virus that is more destructive and damaging? The
argument is not exactly the same. It's analogous, not the same.
But with every advance in technology, there are also
potential downsides. And the point here is to deal with the
downsides and not rely only on the creators to oversee what
they're doing, but impose some rules of the road that protect
them and the industry, as well as the public.
And I think you have been enormously enlightening today to
us. I hope we can continue this conversation because our goal
is to achieve the promise with what you have called, Ms. Toner,
a light touch.
We won't all agree on what a light touch is. But if we are
honest with each other, I think we can develop some standards,
as well as enforcement mechanisms that make sure that we impose
accountability, as well as criteria for judging whether or not
a particular form of AI is safe and effective--just as we would
impose that standard on new drug development. Safety and
efficacy are the standard for the FDA.
I am not going to ask you the $64 billion question, when
will we have generative AI that is as smart as people? Unless
you want to answer that question.
I'll open the floor to you if you would like to give us
your prediction, because that's a horse-is-out-of-the-barn
issue, in a sense. Once it's there and we don't have standards,
you know, it's going to be put on the market. So maybe you want
to address it? You don't have to. Mr. Saunders.
Mr. Saunders. Yes. I think there's--yes, when I thought
about this, there was, like, a, you know, at least a 10 percent
chance of something that, you know, could be catastrophically
dangerous within about 3 years. And I think, you know, a lot of
people inside of OpenAI also, you know, would talk about
similar things. And then I think, right, without knowing the
exact details, you know, it's probably going to be longer.
I think, you know, that--I did not feel comfortable
continuing to work for an organization that wasn't going to
like, take that seriously and do as much work as possible to
deal with that possibility. And I think we should figure out
regulation to prepare for that.
Because, I think, you know, again, it's--if it's not 3
years, it's going to be 5 years or 10 years, you know, the
stuff is coming down the road and we need to have some
guardrails in place.
Mr. Harris. I'm happy to answer that one.
Chair Blumenthal. Mr. Harris.
Mr. Harris. Thank you so much. I think the good news I have
for you is that the answer to that question might not be that
important for you.
The reason I say that is that my background working on AI
is in other areas, not about artificial general intelligence,
but about AI and bias, AI and deepfakes and elections, and,
more recently, AI and harm to children.
That said, I'm lucky to have an excellent colleague at
U.C., Berkeley, Stuart Russell, who's also appeared before this
Committee.
Stuart has invited me to a couple of his conferences, of
his lab, where I am in a room surrounded by hundreds of people
with computer science degrees, Ph.D.s, who've dedicated their
lives and their careers to stopping artificial intelligence
from eliminating our species.
It was a little bit difficult for me, trained sociologist,
to decide where I came down on this issue.
But as the conversation moved to policy and what policies
we need, what I found was that there was almost no disagreement
between me thinking about the problems that I've focused on in
my work and theirs.
The same issues--liability, licensing, registration. Many
of them are also actually quite concerned about deepfakes and
deception.
So, provenance, those key solutions are the solutions that
we need to address the issues of AI bias, discrimination,
disinfor-mation, interference in elections, harm to children,
and the specter of AI, artificial general intelligence, or
superintelligent AI being abused by bad actors to harm us in
catastrophic ways.
Chair Blumenthal. Dr. Mitchell.
Dr. Mitchell. Thanks, I appreciate it. So, I think it's
useful for me to say as someone who works on AI, who has done a
lot of work on rigorous evaluation, this sort of thing, I don't
know what it means for an AI system to have human-level
intelligence.
What I do understand, that I think is related, is to have a
system that can do well on a lot of different tasks, that are
also important for people, that people also do, those kinds of
comparisons.
I think we make a mistake when we group everything together
and say, this is basically like humans. And I want to say that
I think the enterprise of AGI might be inherently problematic
when it's not focusing on what the specific tasks that the
systems should be solving, are.
So, while there's been an interest in AGI so that lots of
different things might be done, it might be beneficial to think
about what those specific things are and creating task-built
models for those specific tasks. And that way we have a lot
more control over what's happening. We can do much more
rigorous analysis of input and outputs. We keep things closed
within the specific domains where a system is meant to be
helpful.
Chair Blumenthal. I think, Mr. Harris, you're absolutely
right. It isn't that important and it shouldn't be that
important because right now we're seeing some of the abuses and
the problems. So, we need to act right now.
And if someone were to say, well, you don't have to worry
about it for 5 or 10 years, I wouldn't believe that person
anyway, because the consequences of getting it wrong are so
disastrous that we should be doing it right now.
It's like saying, well, we don't have to worry about an
outbreak of the next pandemic because, you know, it won't
happen for 100 years. Like, the flu didn't happen for 100 years
after the last pandemic.
Well, but it could have happened 10 years, it could have
happened 5 years afterward, and we live in a world where the
disasters seem to be happening more often, as in climate change
and so forth.
So, I'm going to have to close the hearing. I regret that I
do have to close the hearing because I'm learning a lot. I hope
my colleagues have learned a lot. I hope to continue this
conversation, it is immensely important.
And I thank you, all, for your good work, for the
perspective that you brought to us as insiders, who have seen
this issue firsthand and have chosen to give us the benefit of
your perspective. And thank you, all.
This hearing is adjourned.
[Whereupon, at 4:22 p.m., the hearing was adjourned.]
[Additional material submitted for the record follows.]
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
A P P E N D I X
The following submissions are available at:
https://www.govinfo.gov/content/pkg/CHRG-118shrg63050/pdf/CHRG-118shrg
63050-add1.pdf
Submitted by Chair Blumenthal:
Center for AI and Digital Policy, letter, September 17, 2024..... 2
Public Citizen, Washington, DC, statement........................ 9
Submitted by David Evan Harris:
Supplement to the Prepared Statement, pp. 4-147................. 11
[all]