[Senate Hearing 118-686]
[From the U.S. Government Publishing Office]
S. Hrg. 118-686
ARTIFICIAL INTELLIGENCE IN GOVERNMENT
=======================================================================
HEARING
before the
COMMITTEE ON
HOMELAND SECURITY AND
GOVERNMENTAL AFFAIRS
UNITED STATES SENATE
ONE HUNDRED EIGHTEENTH CONGRESS
FIRST SESSION
__________
MAY 16, 2023
__________
Available via the World Wide Web: http://www.govinfo.gov
Printed for the use of the
Committee on Homeland Security and Governmental Affairs
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
U.S. GOVERNMENT PUBLISHING OFFICE
52-785 PDF WASHINGTON : 2025
COMMITTEE ON HOMELAND SECURITY AND GOVERNMENTAL AFFAIRS
GARY C. PETERS, Michigan, Chairman
THOMAS R. CARPER, Delaware RAND PAUL, Kentucky
MAGGIE HASSAN, New Hampshire RON JOHNSON, Wisconsin
KYRSTEN SINEMA, Arizona JAMES LANKFORD, Oklahoma
JACKY ROSEN, Nevada MITT ROMNEY, Utah
ALEX PADILLA, California RICK SCOTT, Florida
JON OSSOFF, Georgia JOSH HAWLEY, Missouri
RICHARD BLUMENTHAL, Connecticut ROGER MARSHALL, Kansas
David M. Weinberg, Staff Director
Zachary I. Schram, Chief Counsel
Michelle M. Benecke, Senior Counsel
Evan E. Freeman, Counsel
William E. Henderson III, Minority Staff Director
Christina N. Salazar, Minority Chief Counsel
Andrew J. Hopkins, Minority Counsel
Kendal B. Tigner, Minority Professional Staff Member
Laura W. Kilbride, Chief Clerk
Ashley A. Gonzalez, Hearing Clerk
C O N T E N T S
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Opening statements:
Page
Senator Peters............................................... 1
Senator Paul................................................. 3
Senator Lankford............................................. 18
Senator Scott................................................ 20
Senator Hassan............................................... 23
Senator Rosen................................................ 25
Senator Padilla.............................................. 28
Senator Ossoff............................................... 31
Prepared statements:
Senator Peters............................................... 37
Senator Paul................................................. 39
WITNESSES
TUESDAY, MAY 16, 2023
Lynne E. Parker, Ph.D., Associate Vice Chancellor and Director,
AI for Tennessee Initiative, University of Tennessee........... 5
Taka Ariga, Chief Data Scientist, U.S. Government Accountability
Office......................................................... 7
Daniel E. Ho, Professor, Stanford Law School..................... 8
Richard A. Eppink, of Counsel, American Civil Liberties Union of
Idaho Foundation............................................... 10
Jacob Siegel, Writer............................................. 12
Alphabetical List of Witnesses
Ariga, Taka:
Testimony.................................................... 7
Prepared statement........................................... 48
Eppink, Richard A.:
Testimony.................................................... 10
Prepared statement........................................... 72
Ho, Daniel E.:
Testimony.................................................... 8
Prepared statement........................................... 66
Parker, Lynne E.:
Testimony.................................................... 5
Prepared statement........................................... 42
Siegel, Jacob:
Testimony.................................................... 12
Prepared statement........................................... 85
APPENDIX
Statement submitted for the Record by Association for Computing
Machinery 129
Responses to post-hearing questions for the Record:
Mr. Siegel................................................... 141
ARTIFICIAL INTELLIGENCE IN GOVERNMENT
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Tuesday, May 16, 2023
U.S. Senate,
Committee on Homeland Security
and Governmental Affairs,
Washington, DC.
The Committee met, pursuant to notice, at 10 a.m., in room
SD-562, Dirksen Senate Office Building, Hon. Gary Peters,
Chairman of the Committee, presiding.
Present: Senators Peters [presiding], Hassan, Sinema,
Rosen, Padilla, Ossoff, Paul, Johnson, Lankford, Scott, Hawley,
and Marshall.
OPENING STATEMENT OF SENATOR PETERS\1\
Chairman Peters. The Committee will come to order.
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\1\ The prepared statement of Senator Peters appears in the
Appendix on page 37.
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Today's hearing is the second in a series that I plan to
convene on artificial intelligence (AI). At our first hearing
in March, we discussed the transformative potential of AI, as
well as the possible risks these technologies can pose. Today,
we will be discussing how AI has the potential to help
government better serve the American people, such as by
improving the ways agencies deliver services and also what
pitfalls we need to be aware of as government increasingly
adopts AI.
The Federal Government is already using AI in an effort to
provide more efficient services, assess potential security
threats, and automate routine tasks to enhance the Federal
workforce.
Earlier this month, the White House announced new efforts
to invest in American leadership to develop AI technologies and
promote the responsible use of AI within the Federal
Government. Later this summer, the Office of Management and
Budget (OMB) is expected to release new guidance on Federal
Government use of AI, implementing legislation this Committee
advanced in 2020, and was later signed into law in government
funding legislation.
U.S. leadership in the development and use of AI systems,
by both the private sector and government, is essential for our
global economic competitiveness. We should work to ensure that
government can adopt and deploy these tools to help improve
American lives, but as we do so we must ensure we are also
prepared to address the potential risks and harms that AI
systems can present.
The potential for bias in AI applications can have serious
consequences for Federal Government use. A recent study found
that an algorithm used by the Internal Revenue Service (IRS) to
determine who should be audited was erroneously more likely to
recommend Black taxpayers than white taxpayers, and the
government was not prepared with the data or training necessary
to actually recognize this biased outcome.
As we heard in our last hearing, AI algorithms often lack
transparency and accountability for how they arrive at certain
outcomes. Even the engineers who design them do not always
understand how they reach the conclusions that they reach.
In government applications, this can present serious risks
to Americans who may unknowingly be interacting with an AI, and
who may struggle to get answers about why an AI system made a
certain determination.
For example, at least a dozen States deployed algorithms to
decide eligibility for disability benefits, which resulted in
denying thousands of recipients this critical assistance that
helped them live independently, and left them with little
opportunity to understand why the decision was made or how they
could possibly appeal it.
The enormous amounts of data that can be collected as a
result of using AI systems also presents concerns about
privacy. Existing privacy laws do not envision these types of
applications.
As agencies use more AI tools, they will need to ensure
they are securing and appropriately using any data inputs to
avoid accidental disclosures or unintended uses that harm
Americans' civil rights or their civil liberties.
Finally, we must ensure our Federal workforce is ready to
procure and oversee the use of AI systems to ensure they are
benefiting Americans. Last Congress, I authored legislation
that was signed into law requiring officials charged with
procuring AI tools to be trained in both their capabilities to
improve agency missions, and their potential risks, to ensure
responsible use.
Last week, I introduced bipartisan legislation to build on
that effort by requiring Federal agency supervisors and
managers to receive similar training.
I am looking forward to today's discussion and to
continuing to work with my colleagues on both sides of the
aisle to advance solutions that will help encourage American
development of AI, and ensure it is being used appropriately.
During today's hearing, we will be discussing some of those
strategies, including the need to conduct inventories of
current Federal Government AI applications, requiring ongoing
audits to ensure the accuracy and effectiveness of AI systems,
and considering responsible standards that need to be met as
the Federal Government continues to acquire additional AI
tools.
I am grateful to our expert witnesses for joining us today.
We look forward to a fruitful discussion, and a discussion that
will likely continue well beyond this hearing and be engaging
for the foreseeable future.
I would now like to recognize Ranking Member Paul for his
opening statement before we hear from our witnesses. Ranking
Member Paul.
OPENING STATEMENT OF SENATOR PAUL\1\
Senator Paul. In 1975, the late Senator Frank Church said,
``The United States government has perfected a technological
capability that enables us to monitor the messages that go
through the air. That capability at any time could be turned
around on the American people, and no American would have any
privacy left, such is the capability to monitor everything.
There would be no place to hide.''
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\1\ The prepared statement of Senator Paul appears in the Appendix
on page 39.
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These words came as Senator Church led the Senate Select
Committee to Study Governmental Operations with Respect to
Intelligence Activities, better known as the Church Committee.
The Church Committee's 1976 final report exposed numerous
secret Federal programs that violated the constitutional rights
of American citizens it deemed to be threats to existing social
and political order. These programs surveilled and targeted
individuals like Martin Luther King Jr. and domestic
organizations like the Southern Christian Leadership
Conference, as well as infiltrated movements to incite
rivalries and discredit leaders.
Nearly 50 years later, Senator Church's ominous warning
that the government could weaponize technology against the
American people reads more like a premonition. There is truly
becoming ``no place to hide.''
In recent decades, journalists and whistleblowers exposed
examples of our government leveraging emerging technologies to
violate the privacy and civil liberties of its citizens.
Intelligence agencies conducted surveillance of video game
users, collecting data on the contents of communications
between players.
The Department of Homeland Security (DHS) tracked the
locations of individuals and groups participating in the Black
Lives Matter (BLM) movement.
The Drug Enforcement Administration (DEA) conducted
``covert surveillance'' of people protesting the death of
George Floyd.
It is only getting worse. Just last month, the American
Civil Liberties Union (ACLU) acknowledged, ``The Biden
administration has been quietly deploying and expanding
programs that surveil what people say on social media, using
tools that allow agents and analysts to invisibly monitor the
vast amount of protected speech that occurs online.''
How are they doing it? Using artificial intelligence.
For years, Federal agencies, including the Department of
Homeland Security, State Department (DOS), National Science
Foundation (NSF), and the Federal Bureau of Investigation (FBI)
have been colluding with private organizations and social media
companies to combat what they deemed to be ``disinformation.''
Jacob Siegel, in ``Tablet,'' wrote, ``Disinformation is both
the name of the crime and the means to covering it up, a weapon
that doubles as a disguise.'' I think that is an apartment way
of looking at disinformation. It is a tool for those who want
to limit speech, but it also doubles as a disguise and a means
of covering up what they are actually trying to do.
The purpose, so they claimed, was to combat foreign malign
influence. But in reality, the government was not suppressing
foreign ``misinformation.'' It was working to censor domestic
speech by Americans.
Since 2020, the Federal Government has awarded over 500
contracts and grants related to ``misinformation'' or
``disinformation.'' George Orwell would be proud. While the
grant awardees and their proprietary AI and machine learning
technologies differ, their goals are consistent: to ``mine''
the internet, identify conversations indicative of ``harmful''
narratives, track those ``threats,'' and develop
countermeasures before messages go viral. One National Science
Foundation-funded company's mission statement claims that
``social media is being manipulated and ideas are being spread
uncontrollably online.''
The solution it provides? An automatic controversy
detection algorithm to help identify things that are
``potentially opinion-shifting'' in order to make communication
``more productive and less dangerous,'' in other words,
censorship.
During the Coronavirus Disease 2019 (COVID-19) pandemic, we
witnessed the accelerated use of artificial intelligence
technologies to monitor and suppress public debate on issues
like natural immunity, masks, and the origin of the virus.
Multiple Federal agencies, including the Department of Defense
(DOD) and the State Department, funded automated
disinformation-detection technologies designed to monitor and
suppress public debate on issues like vaccines and the origins
of COVID-19.
Writer Jacob Siegel, in a fantastic, yet haunting,
narrative explaining the last decade of U.S. Government
domestic censorship efforts said, ``Disinformation, now and for
all time, is whatever they say it is. That is not a sign that
the concept is being misused or corrupted; it is the precise
functioning of a totalitarian system.''
Make no mistake. The United States is engaging in the same
activities we criticize other countries for. But unlike China
and North Korea, the United States government attempts to
conceal its involvement using private entities as front
companies to do its dirty work.
But make no mistake. The intent is the same. Control the
narrative, eliminate dissent, and retain power.
This should terrify all Americans. The government is using
your hard-earned tax dollars to surveil and censor your
protected speech. Artificial intelligence is only going to make
it easier for the government to do this, and harder to detect.
This should not be a partisan issue. We must get to the
bottom of how the Federal Government uses AI to violate the
privacy and civil liberties of the American people before it is
too late.
Chairman Peters. Thank you, Ranking Member Paul.
It is the practice of the Homeland Security and
Governmental Affairs Committee (HSGAC) to swear in witnesses,
so if each of you would please stand and raise your right hand.
Do you swear that the testimony that you will give before
this Committee will be the truth, the whole truth, and nothing
but the truth, so help you, God?
Mr. Eppink. I do.
Mr. Ariga. I do.
Dr. Parker. I do.
Mr. Ho. I do.
Mr. Siegel. I do.
Chairman Peters. Thank you. You may be seated.
Our first witness is Dr. Lynn Parker. Dr. Parker is the
Associate Vice Chancellor and Director of the AI Tennessee
Initiative at the University of Tennessee (UT). Dr. Parker
spent four years as Deputy United States Chief Technology
Officer (CTO) and Director of the National AI Initiative Office
(NAIIO) within the White House Office of Science and Technology
Policy (OSTP).
Before joining OSTP in 2018, Dr. Parker served as the
interim Dean of the University of Tennessee's Tickle College of
Engineering. She has also served as the National Science
Foundation's Division Director for Information and Intelligence
Systems.
Dr. Parker, welcome to the Committee. We look forward to
hearing your opening statement.
TESTIMONY OF LYNNE E. PARKER, PH.D.,\1\ ASSOCIATE VICE
CHANCELLOR AND DIRECTOR, AI FOR TENNESSEE INITIA-
TIVE, UNIVERSITY OF TENNESSEE
Dr. Parker. Thank you so much. Chairman Peters, Ranking
Member Paul, and Members of the Committee, thank you for
inviting me to testify at this hearing on AI in government. I
am Associate Vice Chancellor at the University of Tennessee,
Knoxville, and Director of the AI for Tennessee Initiative,
where we are working to establish Tennessee as a leader in the
data-driven knowledge economy.
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\1\ The prepared statement of Dr. Parker appears in the Appendix on
page 42.
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My remarks today focus on ways that Federal agencies can
better leverage and govern the responsible use of AI in
advancing their missions and providing services to the American
people.
Federal uses of AI are becoming increasingly transparent as
agencies make available their AI use case inventories in
compliance with Executive Order (EO) 13960 and the Advancing
American AI Act. The extreme variety of Federal AI use cases
creates challenges for developing a flexible approach to the
responsible governance and use of AI by the Federal Government.
To help accelerate the responsible governance and use of AI
in government, I offer the following recommendations.
First, as directed by the AI in Government Act of 2020, and
Executive Order 13960, OMB should prioritize and adequately
resource their work on creating Federal guidance for the use of
AI in government. This guidance should address the wide
diversity of use cases of AI across the Federal Government,
encourage the responsible adoption of AI to improve public
services while protecting privacy, civil rights, and civil
liberties, and be operational for use by the agencies.
Second, Congress should require Federal agencies to use the
National Institute of Standards and Technology (NIST) AI Risk
Management Framework (RMF) during the design, development,
procurement, use, and management of AI. Beginning with a
standardized assessment of the risks posed by use cases of AI
is a key step that can be taken now by all Federal agencies
without needing to wait for additional OMB guidance.
Third, Congress should require every Federal agency to have
a current and regularly updated AI strategic plan that includes
that agency's approach to the responsible adoption of AI.
Fourth, Congress should direct each agency to hire and
resource a Chief AI Officer (CAIO) who is responsible for
overseeing the development and regular update of the
organization's AI strategy and use of AI.
Fifth, Congress should direct the creation of an
interagency Chief AI Officers Council as an effective way to
coordinate the governance and use of AI within the Federal
Government.
Sixth, the proposed Chief AI Officer's Council should
review the agency AI use case inventories for common
application areas and identify dozens of key agency processes
that could be transformed with AI, in a manner consistent with
privacy, civil rights, and civil liberties.
Seventh, Congress should accelerate the responsible and
innovative adoption of AI by providing agencies with AI
innovation funds as part of their annual operating budgets.
Eighth, to help address AI workforce shortages in the
Federal Government, and as directed by the AI in Government Act
of 2020, the Office of Personnel Management (OPM) should
prioritize and adequately resource their work on the AI
occupational series so that Federal agencies will be better
positioned to strengthen their AI workforces.
Ninth, Congress should direct the development of a National
Initiative for AI Education Framework, analogous to the NIST
National Initiative for Cybersecurity Education (NICE),
Framework, that was developed in 2017, to provide a
comprehensive and standardized approach to describing AI roles
and the associated knowledge, skills, and abilities needed for
those roles.
Finally, to help strengthen the breadth and diversity of
talent in the nation's AI ecosystem, Congress should authorize
and fund the National AI Research Resource, as proposed by the
congressionally directed National AI Research Resource Task
Force. Such a resource would help develop new AI talent, with
some of this talent likely choosing to use their AI skills to
support the Federal Government in its adoption and governance
of responsible AI.
I thank the Committee for the opportunity to testify and
look forward to your questions.
Chairman Peters. Thank you. Thank you, Dr. Parker.
Our next witness is Taka Ariga. Mr. Ariga is the Chief Data
Scientist and Director of the Innovation Lab at the U.S.
Government Accountability Office (GAO). As an integral part of
the Science, Technology, Assessment, and Analytics team, he
helps GAO develop and implement advanced analytical
capabilities for its auditing processes.
Prior to joining GAO, he held executive positions at
Deloitte, Ernst & Young and Booz Allen Hamilton, where he
worked with audit, compliance, legal, and regulated entities.
Welcome to the Committee, and we look forward to your
testimony.
TESTIMONY OF TAKA ARIGA,\1\ CHIEF DATA SCIENTIST, U.S.
GOVERNMENT ACCOUNTABILITY OFFICE
Mr. Ariga. Chairman Peters, Ranking Member Paul, and
distinguished Members of the Committee, thank you for inviting
me to participate in today's hearing on artificial intelligence
in the Federal Government.
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\1\ The prepared statement of Mr. Ariga appears in the Appendix on
page 48.
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As GAO's Chief Data Scientist and Director of our
Innovation Lab, I see AI's potential in action every day. But
as you noted in your March 8th hearing, significant risks and
challenges exist wherever AI is assisting or replacing
discretionary decisionmaking.
AI is undeniably an integral part of a functioning digital
fabric. However, the Federal Government is certainly not immune
from consequences of this powerful technology. The need to
promote responsible and accountable use of AI is even more
striking now in the face of growing dangers from unfair,
unintended, or misleading outcomes that carry cascading
societal impacts.
Paradoxically, agencies continue to face acute short of
Federal digital talent needed to implement accountability
practices. We must address both challenges at pace with, and
perhaps even anticipate, rapid advances in AI capabilities.
GAO has issued more than two dozen reports over the past
several years to promote responsible and accountable use of AI.
One of the most notable moments is the introduction of our AI
Accountability Framework in summer of 2021. This first-of-its-
kind blueprint moved beyond high-level aspirations and laid out
33 key implementation practices across four pillars for Federal
agencies to consider as they navigate the AI development
lifecycle. In this framework, we also stress the importance of
taking a team sport approach that integrates perspectives from
an ecosystem of stakeholders.
Beyond GAO's own use across our audit engagements, we are
seeing adoption of the framework by the broader oversight
community to conduct AI evaluations.
On the workforce front, GAO has steadfastly reported on
mission-critical gaps for Federal expertise in science and
technology (S&T) since 2001. The Federal Government, as a
whole, continues to face barriers in hiring, managing, and
retaining staff with advanced technical skills, the very skills
needed to design, develop, deploy, and monitor AI systems.
In our November 2021 report, GAO gathered perspective from
technology leaders across Federal, academic, and nonprofit
entities to explore the concept of establishing a U.S. Digital
Services Academy (USDSA). The aim is to improve the Federal
pipeline of highly trained digital talent that can effectively
and responsibly modernize government, including implementation
of AI systems. Ultimately, having a robust cadre of a digital-
ready, Federal workforce ensures humans can successfully remain
in, and never out of the AI loop.
GAO remains committed to supporting Congress on the ``trust
but verify'' part of the AI accountability equation. We have
formed an internal AI community of practice that includes every
GAO mission team. We have established internal training
resources to enhance data literacy and data science. We have
hired more data scientists. Our Innovation Lab is actively
exploring a variety of impactful machine learning techniques to
transform audit function. We remain engaged with a network of
oversight partners, academic, and governmental entities at all
levels to exchange insights. All of these efforts give GAO
hands-on experience to stay at the forefront of AI technology,
with which to offer technical assistance and strengthen
oversight capacity.
Realizing accountable AI is a continuing journey that
requires a whole-of-government approach, Federal agencies need
more specific guidance on effective implementation of AI. At
the same time, we need practical policy solutions that address
interconnected challenges on privacy, civil liberties, and
workforce readiness.
We know AI capabilities will evolve at an incredible speed,
and the use of AI will continue to diffuse across facets of
governmental functions. GAO believes that the Federal
Government can, and must, simultaneously realize opportunities
afforded by AI and be leaders in good governance, transparency,
and compliance in this age of algorithmic renaissance.
Chairman Peters, Ranking Member Paul, and Members of the
Committee, this concludes my prepared statement. I will be
happy to answer any questions you may have.
Chairman Peters. Thank you, Mr. Ariga.
Our next witness is Professor Daniel Ho. Professor Ho is
the William Benjamin Scott and Luna M. Scott Professor of Law
at Stanford Law School, Professor of Political Science, Senior
Fellow at the Stanford Institute for Economic and Policy
Research, Associate Director of the Stanford Institute for
Human-Centered Artificial Intelligence (HAI), and Director of
the Regulation, Evaluation, and Governance Lab. He also serves
on the National Artificial Intelligence Advisory Commission and
is a Senior Advisor on Responsible AI to the Department of
Labor (DOL).
Professor, welcome to the Committee. We look forward to
your opening remarks.
TESTIMONY OF DANIEL E. HO,\1\ PROFESSOR, STANFORD LAW
SCHOOL
Mr. Ho. Chairman Peters, Ranking Member Paul, and Members
of the Committee, it is an honor to speak with you today.
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\1\ The prepared statement of Mr. Ho appears in the Appendix on
page 66.
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The youth government has an exceptional opportunity. It can
seize this moment of AI innovation to modernize Federal
programs, catalyze scientific advancements, and protect the
rights and benefits of all Americans. Doing so will strengthen
America, but strategic leadership, Federal workforce
investments, digital infrastructure, and procurement
modernization will be necessary. The Federal Government needs
to go from having a few pockets of innovation to a culture of
innovation.
Let me start with why AI in government matters so much.
First, government should lead by example and demonstrate how
responsible AI can modernize Federal programs. In a report we
showed how early AI innovation in nearly half of the largest
140 Federal agencies can transform Veterans Benefits
Administration (VBA), improve monitoring of adverse drug
events, and help protect workers, consumers, and the
environment.
Take the Social Security Administration (SSA), which can
hear over half a million disability appeals per year. With
great foresight, SSA began investing in data infrastructure and
tools to modernize case adjudication in the 1990s. This
culminated in an AI tool that allows judges to use natural
language processing to check draft decisions for some 30
errors, accelerating and improving case processing, leaving
some to call the official who pioneered these early investments
the ``Steve Jobs of the SSA.''
Second, government agencies are, of course, critical for
effective regulation of the risks of AI, and striking the right
balance between innovation and safeguards requires expertise in
government. Getting technical talent into the Federal workforce
is the single biggest obstacle for effective regulation.
Government cannot govern AI if it does not understand AI.
While much progress has been made, including legislation
from this Committee, we still have a long way to go. When our
research team at Stanford examined the implementation of AI-
related legal requirements, stemming from two Executive Orders
and the AI in Government Act, we found a critical gap in
leadership, strategic planning, and capacity. For instance, 88
percent of agencies failed to submit AI plans to identify
regulatory authorities, and the implementation of a key
transparency measure, agency disclosure of AI use cases, has
been inconsistent, and the Office of Personnel Management has
yet to release a required report, due July of last year, to
forecast the AI employment needs and to create an AI hiring
line.
This must change. The Federal workforce does herculean work
but faces fundamental challenges developing teams that can
design, implement, and regulate AI effectively and responsibly.
Stanford's HAI's AI Index highlights that 65 percent of AI
Ph.D.'s land in industry, 28 percent in academia, and less than
two percent in government. Or in the words of one entrepreneur,
``The best minds of my generation are thinking about how to
make people click on ads.''
Strengthening the pipeline of technical talent into the
public sector and providing career paths is urgent. As the
National Security Commission on AI noted, it is not just
compensation. ``It is the perception, and too often the
reality, that it is difficult for digital talent in government
to perform meaningful work.'' I have seen this firsthand. One
Stanford AI Ph.D. student became so frustrated by an agency's
decades-old software stack and lack of computing resources that
he gave up on his aspirations for a career in government and
went back to work in industry.
Let me conclude with four recommendations.
First, strategic leadership from the Federal Government is
required to coordinate and drive forward trustworthy AI
innovation. Congress should borrow a page from the bipartisan
Evidence Act and empower chief AI Officers to ensure that
senior leadership within agencies is driving forward
responsible AI innovation and oversight.
Second, Congress should establish new pathways and
trajectories for technical talent in government. We need better
models--building on the U.S. Digital Service (USDS), public-
private partnerships, and academic-agency partnerships--to
attract AI talent to public service, build cross-functional
teams, and provide pathways for career advancement.
Third, government procurement is critical to capitalize on
American innovation and spur developments of rights-preserving,
privacy-enhancing technologies. We need to move toward more
modular forms of contracting, which the Department of Defense
has illustrated, that enables more effective development,
acquisition, assessment, and auditing of AI systems.
Last, we have to invest in digital infrastructure,
including the National AI Research Resource, endorsed by
multiple Federal task forces, for secure access to
administrative data and large-scale computing resources to
level the playing field. Government data, which is higher
quality, more representative, and reliable than web data that
many models are trained on, is an important part of the
solution. When the U.S. Geological Service (USGS) made Landsat
satellite imagery free to researchers in 2008, it generated $3
to $4 billion in benefits annually, catalyzing discoveries in
habitat modification, climate change, and poverty. That is the
promise of getting public sector innovation right.
The U.S. Government should act expeditiously to foster
responsible AI adoption.
I am looking forward to your questions.
Chairman Peters. Thank you. Our next witness is Ritchie
Eppink. Mr. Eppink serves as Counsel for the American Civil
Liberties Union of Idaho Foundation. He was previously the
Justice Architect for the Idaho Legal Aid Services, and before
that a Fulbright Fellow.
Mr. Eppink, you are recognized for your opening statement.
TESTIMONY OF RICHARD A. EPPINK,\1\ OF COUNSEL,
AMERICAN CIVIL LIBERTIES UNION OF IDAHO FOUNDATION
Mr. Eppink. Thank you, Chairman Peters, Ranking Member
Paul, and Committee Members for your attention to artificial
intelligence and automated decisionmaking in government
programs.
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\1\ The prepared statement of Mr. Eppink appears in the Appendix on
page 72.
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I was invited here today because I have been working for
over a decade with Idahoans with developmental disabilities and
their families to challenge secret decisions made by
computerized algorithmic systems. Only through litigation that
I have helped these families pursue were they able to access
the algorithms the State of Idaho uses to make decisions about
the health care that they depend on day to day.
Once we opened the black box that concealed that automated
system, we found that it was built out of corrupt data, relied
on inputs that the State never validated, and produced results
that even those who created it could not explain. A Federal
court ruled that the system was unconstitutional.
Yet a decade after filing suit and over seven years since
winning in court we are still litigating the case, battling for
due process against still more black box secrecy. Decades-long
class actions by indigent families are not a viable plan for AI
governance in taxpayer programs. We need Federal regulation and
enforcement to protect basic fairness and constitutional rights
in government programs that use AI in automated decisionmaking
systems.
A little bit about the Idaho lawsuit. Although in the past
our society shamefully confined people with developmental
disabilities in State hospitals, today, through Medicaid, they
can get services at home and in their communities instead, at a
savings to the government and to taxpayers.
One of my clients was Christie Mathwig. She was a bulwark
in her rural community of Troy, Idaho, a mother, and a leader
in her church, bible studies, and Al-Anon. She was also
diagnosed as a teenager with a rare neuromuscular disease and
relied on workers in her community for health care and support
throughout her day. Each year, the State assigned her a budget:
not dollars that she would ever see, but reimbursement for her
health care providers, based on assessment results plugged into
an automated system.
Christie came to me when the State told her her budget
would suddenly be cut by tens of thousands of dollars, more
than 20 percent, and not enough for the 24-hour support she
needed to survive. The State refused to provide an explanation,
claiming that the system was a ``trade secret.''
We filed suit, and a Federal court quickly ordered the
agency to disclose the system to us. It turned out to be just a
handful of formulas coded into a basic Microsoft Excel
spreadsheet. As rudimentary as it was, it still took us many
months, three experts, and over $40,000 to reverse-engineer the
system, catalog its flaws, and assess the harm that its results
could wreak upon our clients. We presented our analysis to the
court, and it ruled that the system arbitrarily deprives
participants of their property rights and, hence, violates due
process.
I want to point out three dangers with automated government
decisionmaking that this Idaho litigation, which is known as
the K.W. v. Armstrong lawsuit, illustrates, and then I want to
share three solutions that Congress should enmesh across
Federal programs like these.
First, the dangers. One, black boxes that conceal
government use of AI. If my clients had not found a lawyer with
the time and resources to help, they probably still would not
know that Idaho was using an automated system to make decisions
about them in the first place.
Two, black boxes concealing how these systems work,
including bad data that they are trained on. Once a Federal
court order put Idaho's secret formulas into our hands, it took
a mammoth effort to figure out all the many things that were
wrong with it, including erroneous and corrupt data underlying
it.
Three, black boxes that prevent accountability. Idaho's
Medicaid agency has been fighting again and again, including
just last month, to ban my clients from accessing the very
information they need to challenge the results of its automated
system.
Now toward solutions. There are three that I want to stress
today.
First, the people that these AI systems make decisions
about should be integrally involved in their development,
implementation, and evaluation. This is a cornerstone of the
court-ordered settlement agreement in the Idaho case, and it is
the solution that could prevent the most dangers.
Two, government agencies must implement constitutional
rights through regulation and enforcement specific to AI
systems. Case-by-case litigation, which we know from Idaho is
immensely resource intensive, is not a sustainable solution.
Three, transparency requirements and governance standards
must apply to these systems from before they start until after
they finish. Black boxes have plagued the Idaho system since
2012, and continue through to the present, in 2023. My clients
have a right to the same information the government does to
evaluate these systems and to challenge their results, and
private contractors' proprietary interests can never be allowed
to trump individual due process and equal protection rights.
Thank you for the opportunity to testify. I look forward to
your questions.
Chairman Peters. Thank you.
Our final witness is Jacob Siegel. Mr. Siegel is a writer
and senior editor of News and The Scroll for Tablet magazine.
He previously covered national security and digital culture for
the Daily Beast. His writing has been published in the New York
Times, Politico, the New York Daily News, Vice, Rolling Stone,
and the National Endowment for the Humanities magazine.
Mr. Siegel, welcome. You may proceed with your opening
comments.
TESTIMONY OF JACOB SIEGEL,\1\ WRITER
Mr. Siegel. Good morning, Chairman Peters, Ranking Member
Paul, and Members of the Committee. Thank you for the
opportunity to testify. I am Jacob Siegel, a writer and senior
editor at Tablet magazine as well as a former military
intelligence officer and Iraq and Afghanistan veteran.
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\1\ The prepared statement of Mr. Siegel appears in the Appendix on
page 85.
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Throughout history, warfare has spurred the development of
transformative new technologies. My experiences in the war on
terror provided me with a glimpse of the AI revolution that is
remaking America's political system and culture in ways that
have already proved incompatible with our system of democracy
and self-government, and may soon become irreversible.
I first encountered issues of artificial intelligence in
governance when I was deployed to western Afghanistan in 2012.
What I found there was that in the midst of a great deal of
confusion and ambivalence about the U.S. mission and what we
were still trying to accomplish after a decade at war, the
military had turned to powerful new information technologies
(IT) to fill the strategic void.
On critical fronts like defeating the Taliban and standing
up the Afghan Security Forces, our success remained illusory.
But in the face of this systemic failure the United States
developed a special capacity for building databases.
The theory of data-driven warfare was that collecting
enough information and marrying it with the proper algorithms
into AIs that could perform predictive analysis opened a
technical portal into the future. We could stop the next
improvised explosive device (IED) attack before it occurred,
control events on the ground, and win over the Afghans to our
cause.
It did not work out that way. Years before the United
States withdrawal and the Taliban's return to power, I had come
to see that the gap between our official metrics of success and
the reality on the ground was not only a result of measuring
the wrong things. By translating critical questions of politics
and policy into the language of data, we had outsourced the
most fundamental responsibility of statecraft to machines,
while rendering the essential notions of war, victory, and
peace, obscured to America's leaders.
AI moves us at an exponential rate from obscurity to the
impenetrable darkness of the so-called black box. As the
computer scientist, Stephen Wolfram noted when he testified
before the Senate in 2019, ``If we want to seriously use the
power of computation and AI then inevitably there won't be a
human explainable story about what is happening inside.''
America was founded on the ideal that individual citizens,
through their free and informed actions, should participate in
their government. But for a free people to participate in the
making of their own laws and the meaning of their own lives,
they must be knowledgeable about the world around them.
Centralized applications of AI that invisibly alter the
architecture of perception and reality, for instance by
performing mass censorship of certain phrases or narratives,
makes such knowledge impossible.
Moreover, as the writer, James Poulos, has noted, ``The AI
approach to governance undermines individual's faith in their
own capacity for reason to action because it is driven by a
logic of seeing technology as better and stronger than
humanity.''
In other words, a technology that is intrinsically
threatening to human interests, with a potential
transformational power on the order of the printing press or
the wheel, which is, at this moment, being funded and deployed
by multiple government agencies, appears destined to move
further away from human understanding the more it progresses.
Yet there is no chance that the U.S. Government and U.S.-based
corporations are going to abandon a technology this powerful,
nor would such an outcome necessarily be desirable, given that
it would cede the space to competitors like China.
We seem to be caught in a trap. There is a vital national
interest in promoting the advancement of AI, yet at present the
government's primary use of AI appears to be as a political
weapon to censor information that it or its third-party
partners deem harmful.
Example abound from recent years of this kind of AI-driven
informational control system, which is deployed at every
opportunity in the name of public safety and emergency. It is
in the name of safety that government officials are now calling
for even more control over AI.
Earlier this month, Jen Easterly, the Director of the
Cybersecurity and Infrastructure Security Agency (CISA) called
for more regulation of AI, warning that we need to be very
mindful of making some of the same mistakes with artificial
intelligence that we have made with technology. But regulating
AI so that it becomes an even more powerful tool of censorship
for enforcing party orthodoxies will increase neither or safety
nor our security.
Easterly also recently argued that China has already
established guardrails to ensure that AI represents Chinese
values, and the United States should do the same. While
emulation of the Chinese model of top-down, party-driven social
control appears to be the direction that AI in governance are
moving in the United States, I would submit, respectfully, that
continuing in this direction will mean the end of our tradition
of self-government and the American way of life.
Thank you.
Chairman Peters. Thank you, Mr. Siegel.
Mr. Eppink, in your testimony you told us about how you are
suing the Idaho Medicaid program for its failure to disclose
the algorithm that it used to substantially cut recipients'
health care services. But could you tell the Committee more
about how your clients even learned about the use of this
automated system?
Mr. Eppink. Certainly, Chairman. The Idaho system, when we
got started, I think like many other systems around the country
probably still today, was not disclosed to anyone outside of
the Idaho agency, so far as I know, and after about a half
dozen families had contacted me, scared that their lives would
be upended, I figured I would send a letter, which I did, and I
got one back from the agency's lawyers saying that the system
was a trade secret. Once we knew it was a secret, we had to go
from there to file a lawsuit to find out more about what the
system was.
Chairman Peters. Right now we are hearing an awful lot
about generative AI tools, like Chat Generative Pre-Trained
Transformer (ChatGPT). It is all over the news. Everybody is
chatting about it. It is a hot topic.
The system that you are talking about was relatively simple
and an older, automated system compared to what we are seeing
right now. What are your thoughts on that fact and how it
should inform our efforts to set Federal AI policy?
Mr. Eppink. Yes. I guess for starters, black boxes are
black boxes, no matter how big they get and no matter what is
inside of them. Even though the Idaho system was just an Excel
spreadsheet, it still ended up taking us a significant amount
of time--I think it was maybe months, possibly a matter of
years--to get all of the information on which it was built.
Federal AI policy standards and enforcement that are
governing use of automated systems and AI are not solving
necessarily for the complexity of those systems. They are
solving for the harms that they are causing.
I do not know that we need new standards for each new
technology. I think the same principles that we can apply to
protect and prevent some of the things that we have had to
litigate in Idaho would apply to more complex systems as well.
Chairman Peters. Clearly, as I listen to your testimony,
you believe that there is simply just not enough transparency.
What would you recommend when it comes to holding these
platforms more accountable?
Mr. Eppink. What is critical is standards and enforcement
that are specific to AI and automated decisionmaking systems,
which we have had to enforce in Idaho through the litigation.
We have basic American principles like due process and equal
protection that are there. Where our courts struggle and where
our agencies still have gaps as far as what they are overseeing
is how to apply those in this new context to these automated
systems. We know that the litigation in court, I think, is too
unwieldly. Although we have done it in Idaho, it is not going
to meet the proliferation of these systems.
Each agency, for instance, with the Medicare program in
Idaho, the Centers for Medicare and Medicaid Services (CMS)
should be overseeing programs that are using automated systems,
like Idaho, and making sure that they are complying with
standards that are specific to those programs. We already have
a lot of jumping-off points that can be used to develop those
standards. They just need to be put in place and then enforced
by these agencies.
Chairman Peters. Thank you.
Dr. Parker, I have two questions for you. First, some
government uses of AI are clearly high risk, like when the
Department of Homeland Security uses facial recognition
technology. On the other hand, there is also some low-risk use
of AI, like when the Forest Service classifies tree canopy
coverage with machine learning tools.
The first question. Should testing, auditing, and
procurement requirements be different, depending on the type of
system, and two, how should the Federal Government decide what
is actually high risk and what is low risk?
Dr. Parker. Thank you for the question, Mr. Chair. There
are certainly many types of AI systems, and I think what is
getting attention a lot in the press these days are some of the
extreme cases. But we want to also encourage the use of AI in
the mundane cases because they can improve services for the
American people and they can improve efficiencies.
NIST has come out recently, at the direction of Congress,
with a NIST AI Risk Management Framework. I think evaluating
the risks of each individual use case is important to have the
right governance approach. We do not need onerous regulations
and oversight of simple use cases that no one believes are
going to harm anyone.
Having that overarching policy, that for every use case of
AI within the Federal Government those agencies step through
the NIST AI Risk Management Framework, to determine what is the
risk level. If it is low, then the steps that are needed in
order to make sure it is used safely are much more minimized.
If it is more consequential, then, of course, we do need to
have much more oversight. Certainly GAO's Accountability
Framework or the Blueprint for the AI Bill of Rights are
designed to try to inform how to address some of those high-
risk uses.
Chairman Peters. Thank you, Dr. Parker. You mentioned the
Government Accountability Office, so this question is for you,
Mr. Ariga. In your testimony you mentioned how the GAO has
created a toolkit to audit the AI system used by government,
and that you are piloting that toolkit right now with one
agency.
My question for you is, can you tell us more about how the
pilot is going, what roadblocks you may be facing, and at this
time would it be feasible for all agencies to use the GAO
Accountability Framework to audit their AI systems, and if not,
why?
Mr. Ariga. Thank you, Chairman Peters, for that question.
GAO published our AI Accountability Framework back in the
summer of 2021, and it is really a first-of-its-kind blueprint
for agencies to consider as they navigate the AI development
lifecycle. The basic premise is that if agencies can implement
practices, entities like GAO can then come afterwards to
identify and evaluate those, and point out any findings and
recommendations there may be needed.
Certainly GAO has a number of ongoing audits using the AI
Accountability Framework, and we are also hearing that other
oversight entities are also using the framework itself to
conduct their AI evaluations.
There are three challenges that particularly, I think,
hamper the AI development within the Federal Government space.
One is that while our framework looks at the AI development
lifecycle, agencies can use more domain-specific guidance. For
example, the ingredient list for transparency in medical
diagnostics may look very different than facial recognition or
even autonomous vehicles.
We also need policies around the notion of privacy,
transparency, and compliance. And then last, we certainly need
a digital-ready workforce to implement those practices in a way
that keeps humans in the loop.
Chairman Peters. Great. Thank you.
Ranking Member Paul, you are recognized for your questions.
Senator Paul. When I first read 1984 I was concerned but I
realized at the time we did not have the technology for 24/7
surveillance and we did not have two-way television sets. There
was not this ability to abuse our rights in such a systematic
fashion. We do have that technology now, so it is of more
concern. But I still think it is a mistake to concentrate on
the technology and not the concentration of power. I, for one,
do not fear AI at all if the Bill of Rights were protected. I
think it really is a question of whether or not we would allow
AI to invade our rights as protected by the Bill of Rights.
Mr. Siegel, do you believe it is possible for the
government to define and police disinformation without
infringing on the First Amendment?
Mr. Siegel. I think, Ranking Member, that if it was a
strictly defined category of disinformation that applied
specifically to foreign actors that had strict limits on its
application, that it might indeed be possible but then in
practice that is, that is not what its going to do.
Senator Paul. I think that makes a good point. The Bill of
Rights does not necessarily apply to every person living in
Libya or Sudan or any other different country. We surveil them
all the time. We collect all of the phone information. I think
in one month we collected every phone call in Italy. I do not
know why Italy, but we collected every phone call in Italy. The
only way you could possibly get through that is with some kind
of AI program. We have like thousands and thousands of hours of
audio. The only way you could get through that is with help.
But this is war we are talking about, when we are
interacting with foreigners. When we are talking about us, we
are supposed to have the Bill of Rights as protection.
I guess I would make the point that I do not think
disinformation for the U.S. citizen, I do not think it can be
defined. I think it is in the eyes of the beholder. If you are
willing to police information you run a great risk of
infringing the First Amendment. I do not know how you do define
disinformation.
For example, in the Virality Project, which was from
Twitter, but it also was Twitter working with the FBI, and the
FBI paid Twitter for the information, they explicitly said that
we need to take down things, even if they are true, if they
might breed vaccine hesitancy. If someone did die from a
vaccine and it is absolutely true, we are still going to take
it down because the people are not smart enough to understand
and have this information.
Do you think for domestically, maybe part of the solution
would be saying that we restrict this type of technology from
being used to limit or hinder protected speech?
Mr. Siegel. Yes, Ranking Member. I think that in the
domestic context it is inherently if not unconstitutional then
direct infringement on the Constitution, potentially, at least,
and that it is also worth pointing out that from the origins of
the modern U.S. counter-disinformation establishment in 2016,
there are explicit references to the strategic difficulty in
distinguishing between foreign and domestic actors.
The origins of the Global Engagement Center (GEC), for
instance, in 2016, there is already talk about how the
internet, from the leaders of the GEC, about how the globalized
nature of the internet makes it so that what are referred to as
old-fashioned privacy laws are now a hindrance on the proper
collection of what is considered to be relevant information in
surveillance purposes.
I think that in the domestic context not only is there an
inherent difficulty in enforcing any standard of disinformation
that is not inherently politicized, it also appears to be very
difficult to make strict legal distinctions between foreign and
domestic actors that are then enforced.
Senator Paul. The simple way of narrowing it down is to put
it in the context of the Bill of Rights, basically that if it
is a right that is protective, AI should not be used in any way
to define or limit that right. The forest canopy, I do not care
of you use AI. Overclassification, we have 25 million records,
and we want to run AI through it. My son writes the code for
AI, so I am not against AI in any way, but it just needs to not
infringe on speech.
On the medical example, you have to have a human. Even with
the overclassification problem, if you have an AI trolling
through there to point out what could be declassified, still
some human is going to have to look at it to declassify it at
some point, but it helps to organize things.
Really this is about protecting the Bill of Rights. But
what disappoints me is in the current political atmosphere is
there is not really so much a bipartisan concern for this. I
have heard almost nothing from the other side on the idea that
the FBI was going to Twitter and saying, ``Take down this
information. We think this does not meet your policy,'' and
Twitter is going, ``Well hell, it is a lot of work. Will you
pay us?'' The FBI is saying, ``Yes, we will pay you to take
down this information.'' They say, ``Is all of it
disinformation, not true?'' ``Some of it is true. We just do
not think it is helpful for society to read this. It is not
helpful for them, even if it is true.''
That should scare us all, on the right or the left, and yet
the outrage seems to have been largely one-sided.
For example, I have a bill, and it simply would say this:
nobody in government can meet with, collude with, or work with
anybody in media of any form to limit or restrict protected
speech. The Constitution, the courts have defined protected
speech. It would largely keep them out of this. Some people are
like, ``Oh, people would say things are untrue.'' Yes, but
unfortunately the First Amendment allows bad speech, allows
people to say terrible things, and occasionally would allow
people to say something. But most of the time there is actually
a debate, with evidence on both sides, and that is what the
news media is about, trying to figure out what the truth is. It
is not like one source has all the truth.
I think if we could get back to more discussing the Bill of
Rights in the context of artificial intelligence I think we
would have a better chance of getting to the solution. Sure,
will there be some specific controls on artificial
intelligence? Yes, but it is not so much that we should fear
technology. We should fear the technology in the hands of
people who would abuse our liberty.
But thank you all for appearing.
Chairman Peters. Thank you, Senator Paul.
Senator Lankford, you are recognized for your questions.
OPENING STATEMENT OF SENATOR LANKFORD
Senator Lankford. Mr. Chairman, thank you. Thank you all
for being here and being in the conversation. Obviously, this
is an ongoing conversation. I want to drill down on a couple of
comments that a couple of you made and a couple of you have
implied on this.
Dr. Parker, you made the statement, ``responsible use of
AI.'' Mr. Ho, you made the statement, ``responsible AI and AI
innovation.''
OK. Define for me what ``responsible use of AI'' is, and
maybe that is the flip side of what irresponsible AI is.
Dr. Parker. Thank you for the question, Senator. I think
this is the million-dollar question, and this is the reason why
I think guidance from OMB, for instance, as directed in the AI
in Government Act and the Executive Order 13960 is so critical,
because that process that agencies are expected to follow to
ensure that their AI is used in a way that is upholding of our
expectations for whatever responsible means is key. I think by
defining the processes that agencies must abide by as they look
at their use cases of AI, look at the risks of AI, that will
inherently help us understand what is responsible.
But if you look sort of theoretically at what responsible
AI means, there are a number of principles that around the
world we have converged on as those characteristics of AI that
we agree fall into either what we call ``responsible'' or
``trustworthy'' AI. These are things like safe and effective.
They are the way that the systems actually work is consistent
with their intended use. We have accountability. There are a
number of these kinds of principles that taken as a whole can
be reflective of our sense of whether or not the AI system is
trustworthy or whether or not it is responsible.
To summarize, I think if we have AI systems that we agree,
through a set of processes, that I think should be defined by
OMB, that allows those AI systems to demonstrate those agreed-
upon principles, then I think we would call that
``responsible'' use of AI and ``trustworthy'' AI.
Senator Lankford. OK. Mr. Ho, do you want to take a stab at
that?
Mr. Ho. Sure. I agree with much of what Dr. Parker said. I
think there actually is a fair bit of agreement when you look
at various frameworks, the Executive Order 13960 on trustworthy
AI within government, the Blueprint for an AI Bill of Rights,
the NIST AI Risk Management Framework, and the GAO framework,
that Taka Ariga mentioned. There have been attempts to try to
actually look at the agreement, and there actually is fairly
large agreement across these in terms of privacy protections,
nondiscrimination, safe and effective forms of AI, and human
awareness of how they are being used.
I think the frontier of all of this is really how to take
these principles and bring them into practice, and that is
where having the requisite technical talent and leadership
within government agencies is going to be so absolutely
critical. Because we can all agree on principles in practice,
but trying to actually drive them into operation I think is a
really important frontier.
Senator Lankford. Much of the AI conversation really boils
around not just a responsible use but the data that is behind
it. If the data is not good, then the whole system is going to
be bad.
Here is the fear that I hear from people is that if a wrong
dataset ends up into the mix here and it affects you and your
family, as you have dealt with in Idaho, and you have a false
dataset or a false algorithm or something that has a glitch in
it, you can be directly affected by that and you do not even
know how to be able to reach into it.
All these different AI researchers now are all trying to
get more access to government data, No. 1, because it is
perceived to be free, when actually the taxpayers paid for
this, but they want to get mass amounts of data, they want mass
amounts of faces, they want mass amounts of information. The
Consumer Financial Protection Bureau (CFPB) just came out with
a new rule for banks, for loans they want 80 different
datapoints, and they want to make those publicly available.
There is a huge amount of data that government is pulling
and making it more and more available, where it is easier to
connect the dots and to be able to identify individual people,
where maybe that dataset does not identify that person, but it
is not hard to get three different datasets, combine them
together with their cellphone records that are publicly
available for that data point, and to be able to identify this
person and all of their habits, all of their locations,
everything else.
There is a concern on the availability of data that is out
there and the continual push by every entity to say, ``We want
our AI to be better. Our AI can be better, we want more data on
every individual.'' Set that aside with responsible use of AI
and with privacy, because right now privacy seems to be losing
the battle, and so people can do more with AI because they
could if they had more data.
Where am I off?
Mr. Ho. Senator, thank you for that exposition. I think
privacy is absolutely paramount, and I think as the number of
the panelists have noted, a national privacy legislation would
be quite important here, where currently we have a system that
is a kind of patchwork system.
I think the other dimension that I would point out is that,
I think going back to what Ranking Member Paul noted at the
beginning in terms of the conversations in the 1970s, Congress
made a choice to enact the 1974 Privacy Act that really only
reached sort of the data that government agencies have, and
really did not reach kind of the private sector.
I think some of the places where we see the most acute
concerns of hoovering up lots of data and identifying
individuals is in the small number of technology companies that
have scraped the entire World Wide Web and they are building
models off this. That is why I think coupled with comprehensive
privacy legislation, I think what is really needed is a kind of
data strategy, and I think there are some really good
blueprints for this in the National Secure Data Service that
came out of the Evidence Act, that is trying to put in a series
of kind of safeguards to really ensure that it is the right
people with the right safeguards who have access to data. That
is the same kind of blueprint that is being used for the
National AI Research Resource, so that when folks have access
to administrative data it is done in a secure and privacy-
protecting way.
Senator Lankford. Does anybody else want to make a comment
on that?
Mr. Eppink. Two other things I might add, Senator, on data.
One is to recognize that, as I have talked about a little bit,
the costs and the time necessary to analyze this data for
someone who these systems are making decisions about is
inaccessible. In the data, often, especially when you are
talking about my clients who are people with developmental
disabilities, is corrupted not only potentially by the creation
of the data in the first place but by years and years of
discrimination and other effects that have biased the data in
the first place.
Senator Lankford. Thank you, Mr. Chairman.
Chairman Peters. Thank you.
Senator Scott, you are recognized for your questions.
OPENING STATEMENT OF SENATOR SCOTT
Senator Scott. First, thank you, Chairman Peters, for
holding this hearing.
Artificial intelligence surely has productive uses, but it
can also present great threats, especially to our children.
Today I am introducing my AI Shield for Kids (ASK) Act, to
prevent children from accessing artificial intelligence
features on social media sites without the consent of a parent
or guardian.
I have long been a supporter of doing more to keep our kids
safe online. There is no doubt that we must do more to combat
the emerging threats our children face each and every day on
the Internet. Like probably some other people here, I have a
bunch of grandkids, and I do not ever want to put them at risk.
Mr. Ariga, AI poses significant risks to Americans,
particularly vulnerable Americans such as children and youth.
There are threats to privacy, user manipulation, and safety
concerns. We know that AI regulation is lagging behind the
speed of AI development and use. What guardrails are needed to
protect vulnerable Americans from threats from intrusive AI,
such as Snapchat's chatbot?
Mr. Ariga. Thank you, Senator Scott, for that question. For
us at GAO, we certainly believe in the ``trust but verify''
part of that equation. We want to be able to assess implemented
practices that agencies have adopted to make sure that they are
in line with the internal control principles as well as in the
areas of privacy protection and safeguards that you have
mentioned.
Within our AI Accountability Framework we have actually
laid out specific practices in the areas of governance, data,
performance, and continuous monitoring to make sure that the
agencies themselves have infrastructure as well as evidence of
those implementations, and in areas where we find potential
recommendations and areas of improvement we will certainly
issue findings in our report as well.
Senator Scott. Snapchat admitted that their AI technology
is experimental, so do you agree that it is alarming and
problematic that Snapchat would force their users, which
comprise almost 60 percent of American teens, to use their
chatbot features unless they paid to disable it with a
subscription service?
It is hard to believe. If you do not want it, you have to
pay to get it off, if you want to keep using Snapchat.
Mr. Ariga. Yes, I will answer that. GAO's role is to
provide rigorous oversight. Should agencies decide to use any
AI technology, including Snapchat, we certainly stand by, ready
to do the rigorous programmatic assessments of those programs,
in line with what we have laid out in our Accountability
Framework.
Senator Scott. Do you think you ought to have to pay to get
Snapchat off, The AI off your app that you want to use?
Mr. Ariga. We will be interested in hearing the legal
rationale and any other governance structures that arrive at
that decision.
Senator Scott. How about parents? Do you think parents and
guardians have the right to protect their children and revoke
consent without being charged a fee?
Mr. Ariga. Certainly GAO has done a number of work around
education, around childhood development, and so we would sort
of take that similar approach at looking at programmatic
implementation to see how that aligns with what we have laid
out in the Accountability Framework.
Senator Scott. Do you agree that you ought to get consent
of parents for teenagers?
Mr. Ariga. Again, I will go back to whether those legal
rationales were done in a deliberately and sound way.
Senator Scott. Do you have kids?
Mr. Ariga. Yes, I do, two.
Senator Scott. OK. Do you watch what they look at?
Mr. Ariga. For sure. When my daughter looks at YouTube
videos I certainly want to make sure that the content is
appropriate for her age. As a parent I certainly share the same
concerns that you do have. But from a GAO perspective, we try
to be specific with our oversight role to say as agencies
implement these capabilities there are specific sort of
guardrails and expectations from internal controls that GAO
will be looking at.
Senator Scott. As a parent, do you think that you ought to
have to pay to get AI off something that your teenager uses?
Forget your job. Just as a parent, what do you think?
Mr. Ariga. Fortunately, I do not have a teenage kid just
yet. But eventually I think I will have to grapple with that
reality, in addition to all the subscription fees that we have
already paid for.
Senator Scott. It appears that unelected administrative
officials in DHS and CISA and others in this Administration
have urged censorship on disinformation. It is frustrating when
people say the border is secure and the laptop was not real,
stuff like that.
Mr. Siegel, as a writer, can you talk about the dangers of
Big Government colluding with Big Tech and corporations to
censor journalists like the New York Post on a story that was
true?
Mr. Siegel. Yes, Senator, thank you. I can address that. I
think the dangers cannot be overstated. I think that kind of
collusion between government and Big Tech and interference in
open discourse in the political system is, strictly speaking,
incompatible with the continued practice of democracy and self-
government. I do not think that you can have free and fair
elections when there is mass censorship occurring at scale,
when there is collusion between various intelligence agencies,
Federal agencies, not only Big Tech corporations but also other
third-party, nonprofit actors who are essentially operating in
a para-governmental role.
I do not think that it is simply a question of censorship,
though in a technical sense censorship is what is occurring and
certainly that is bad enough. It seems to me to be a
surreptitious form of government, that there is a form of
governmental authority and control being exercised through
these relationships, determining how Americans are conditioned
to perceive various policies and acts of government, ranging
from things related to public health and vaccines to foreign
wars. That insofar as sovereignty has been secretly transferred
from individual citizens to these new relationships of power,
it seems to me incompatible with the traditional American
system of democracy.
Senator Scott. What do you think about the dangers and
risks of the government potentially funding and moving toward
algorithms that use AI technology to censor Americans online?
Mr. Siegel. I think it is a related sense of risk, a
related set of risks. I think that the greater risk, what we
have not fully seen yet, is censorship that is effectively
invisible because it uses AI to trap speech and narratives on
the wire, as the phrase goes, meaning that rather than waiting
for the New York Post to publish something, and then Twitter,
Facebook, and other Silicon Valley companies operating under
the direction of Federal Government agencies then censoring
that information, potentially what we see in the future is AI
being used to censor information before it is ever published.
That could happen in forums where we have come to expect mass
censorship and which we view as public forums like on Facebook
or on Twitter, for instance. But it could also happen on what
you might think of as the back end.
For instance, Google was censoring Google Docs during the
pandemic. There was a white paper on hydroxychloroquine that
was published to Google Docs, and I think most people's
understanding of Google Docs is that it is a kind of semi-
private document that Google has given to you as one of its
suite of services. But that illusion of ownership or control is
just that--it is an illusion. This paper, white paper, whatever
one thinks of its contents, was deleted by Google without
explanation, without reference to any formal policy violation.
It is those kinds of invisible censoring acts occurring at
scale that I think AI could drive, and it would be an even more
significant threat than what we have already seen.
Senator Scott. Thank you. Thank you, Chairman.
Chairman Peters. Thank you, Senator Scott.
Senator Hassan, you are recognized for your questions.
OPENING STATEMENT OF SENATOR HASSAN
Senator Hassan. Thank you very much, Mr. Chair, and I want
to thank you and the Ranking Member for holding this hearing,
and I really want to thank the witnesses for being here today.
Thank you for sharing your expertise and perspectives with us.
I want to start with a question to you, Dr. Parker.
Congress created the National AI Initiative Office to improve
efficiency and help Federal and local governments, researchers,
business leaders, and other stakeholders collaborate on AI
issues. You were the first National AI Director, but that
position has remained vacant since you left the post in August.
As the former Director, why is it important that we fill
this position?
Dr. Parker. Thank you, Senator, for that question. The
National AI Initiative Act of 2020 set up three very important
goals for our nation about us leading the world in AI research
and development (R&D), about making sure that we also lead in
the development of trustworthy AI and both the development and
use of trustworthy AI in both the public and private sectors,
and to make sure that we are educating and training our
workforce to participate in these AI activities.
It also put in charge of these activities the National AI
Initiative Office, and that is in addition to a coordination
role among all the Federal agencies and what they are doing in
the AI space.
Given the magnitude of the importance of the National AI
Initiative, as set forth by Congress, I think it is imperative
that we have leadership in the White House that is overseeing
these activities, that is pushing forth the innovation and the
research that we need to address many of the kinds of concerns
that have been raised in this panel so far, to make sure we are
training and educating our workforce and leading new
initiatives across the government that could help advance
these, and the governance issues that we are addressing today.
That leadership vacuum I think has contributed to a number
of challenges that we have across the board, in terms of being
able to implement these good policies that are in place.
Senator Hassan. Thank you very much for that.
To Mr. Ariga, AI researchers have highlighted some of the
potential public safety risks posed by AI systems. These safety
concerns include AI systems providing dangerous information to
bad actors or potentially acting in unpredictable ways that run
counter to the intent of designers. Increasing the safety and
predictability of AI systems requires more technical research
into the methods used to create these systems. What can the
Federal Government do to support or coordinate research that
would help improve the safety of AI systems?
Mr. Ariga. Thank you, Senator Hassan. In our AI
Accountability Framework we laid out specific governance
practices where agencies can adopt to really consider, for
example, is AI even necessary in this particular use case? In
fact, if it is, what are some of the organizational structures
that are in place to assess the legality, the compliance
factor, as well as driving some of these inherently
governmental functions?
We go back to our framework itself that lays out not only
governance but looking at data performance, as well as the
important topic of continuous monitoring when it comes to AI.
Senator Hassan. Thank you. Another question for Dr. Parker.
Congress created the National Artificial Intelligence Strategy,
as you noted, to establish goals, priorities, and metrics for
guiding and evaluating interagency work on AI. That strategy
focuses on things like AI research and investments in workforce
development. However, the strategy does not currently require a
strategic level focus on safeguards to prevent AI from being
used in a manner that harms our country or society.
Can you speak to how a focus on AI safeguards could be
incorporated into the National AI Strategy?
Dr. Parker. Yes. Thank you, Senator. I think it is true. If
you look at, say, the role of the National AI Initiative
Office, it is to oversee the research and the education and the
coordination, but it does not provide a role for that
governance. We need an approach to be able to govern that
responsible use of AI.
I think one thing that could be done is to have kind of a
two-part approach. One is to have Chief AI Officers at every
agency that are responsible for these kinds of activities
within each individual agency. This has been a challenge
because even in the Executive Order 13960, that requires
agencies to identify a responsible AI official, that has not
happened well. We do not have a single point of contact or a
single responsible person at the agencies that oversee these
activities.
But agencies all have different missions, and so in order
to coordinate in a consistent way across all of the Federal
agencies I think the creation of something like a Chief AI
Officer's Council, that is led by OMB and OSTP's National AI
Initiative Office, perhaps with representation from the General
Service Administration (GSA's) Center of Excellence (COE) in AI
as well as the Community of Practice would provide the
expertise across the Federal agencies to coordinate these
processes and provide the leadership for the government as a
whole.
Senator Hassan. I think that makes a lot of sense, and,
when you think about emerging technologies generally we tend to
focus on the potential of the technology without thinking about
the necessary safeguards, in my view, early enough. I think a
lot of what you have heard around the dais this morning is
concerns about the safety issues and the way that AI can impact
our democracy, but we need to be thoughtful about how we can
actually address those issues in a way that is consistent with
our values. I appreciate your answer.
I do have another question for you, Dr. Parker, on deep
fakes, which are obviously images and videos that are generated
artificially, and they are becoming increasingly realistic.
Last year, malicious actors deployed a deep fake video of
Ukrainian President Zelensky telling Ukrainian soldiers to lay
down their arms and surrender to Russia, for example. As
artificial intelligence advances, deep fakes like the Zelensky
one will become harder to identify and debunk, and I am
concerned that in the hands of our adversaries, deep fakes pose
a really significant threat.
How do you assess the Federal Government's current efforts
to identify and debunk deep fakes? How can the Federal
Government prepare for a future with extremely realistic deep
fakes?
Dr. Parker. Thank you, Senator. I am a technologist, and so
I think often of these challenges from a research perspective
and what we can do in terms of coming up with new approaches
that help us address these kinds of challenges. There are some
activities in the research space to do things like
watermarking, that can allow you to determine how a particular
piece of data or an image or a video, where it came from, what
we call the provenance of it, what its history is.
Those kinds of approaches, if we could watermark these
kinds of images and content in a way that allows us to trace
back its origins and where it came from, and is it real or is
it not real, that is a step forward to giving us the technical
ability to address these kinds of challenges.
On top of that, of course, are the governance approaches,
and frankly, I did not do a lot of work myself in government
work and the governance of deep fakes. But I think the
technical approaches that I mentioned will help, and so
certainly an increased attention to those kinds of technologies
is helpful.
Senator Hassan. Thank you, and I am over time. I will
follow up with one of the questions I think we will face, Mr.
Chair, is how does the government work with the private sector
and academic to try to make sure that we are harnessing various
ideas and approaches here. Thank you very much.
Chairman Peters. Thank you, Senator Hassan, and actually,
that topic is going to be a future hearing, because it is
incredibly important. We will look forward to discussing it at
length.
Senator Rosen, you are recognized for your questions.
OPENING STATEMENT OF SENATOR ROSEN
Senator Rosen. Thank you, Chairman Peters. I really
appreciate you holding this hearing today, and thank you to all
the witnesses, for all you do, for your work and your
thoughtfulness in this really important area.
Of course, we are talking about standards, and all of us
are worried about technology moves faster than we can
oftentimes even adapt to it and figure out what we need to do.
Standards are really important. We know that China has explicit
plans to become a standards-issuing country, and part of its
push to increase global influence, it coordinates national
standards work across government and industry. China's
strategy, they involve targeting emerging technologies like
quantum computing, big data, 5G, artificial intelligence, where
the global rules really have yet to be defined.
In order for the United States to remain a leader in AI and
maintain a national security edge, our response must be one of
leadership, coordination, and above all, cooperation, and this
means working, like Senator Hassan said, with the private
sector and academia, investing in R&D for emerging
technologies, coordinating with relevant agencies, and engaging
with international standards-setting bodies.
Mr. Ho, can you describe the importance and impact of U.S.
participation in these international standards-setting bodies
for the development of emerging technologies, including AI?
Mr. Ho. Thank you for that question, Senator Rosen. I think
you may have been one to introduce legislation that actually
fosters international cooperation, and I think that is exactly
the kind of effort that is required right now in this context
of geopolitical competition, where if it is possible to have
international cooperation schemes with like-minded countries,
there is a way really to address this current question of the
concentration of who really builds, owns, and guides these
kinds of AI systems.
One proposal that is very much in line with what you
proposed earlier is the proposal for the Multilateral AI
Research Institute, to enable like-minded countries to
collaborate together, to engage in this kind of standards-
setting. I think that is an absolutely critical potential path
forward for the future.
Senator Rosen. Thank you for bringing that up because I do
believe by doing nothing we are actually doing something. That
leads me to my next question, to Dr. Parker, and again, to you,
Mr. Ho.
Earlier this year, the National Institute of Standards and
Technology did release an AI Management Framework, with the
goal of improving trustworthiness of artificial intelligence,
like we are talking about deep fakes and others. About three
months later, the White House Office of Science and Technology
Policy issued its Blueprint for an AI Bill of Rights.
How should the private sector view these two bills? Are
they complementary? Do we need to merge something into one?
What are both of your opinions on this.
Dr. Parker. Thank you for that question, Senator. The NIST
AI Risk Management Framework is a framework that applies, in my
opinion, to any use case of AI. It gives you a standard
approach to be able to consider any given use case of AI, and
step through a number of areas as it relates to identifying
risk and governing and managing them, and so forth.
The answer there may be that that particular use case of AI
is low risk, so the additional steps that might be needed to
govern it might be a few, or it could be much more substantial.
The Blueprint for the AI Bill of Rights that the White
House OSTP issued is coming at the challenge from a very
specific category of applications of AI, and these are
applications of AI that may harm individuals or community
groups or society in terms of your civil rights and civil
liberty and privacy, your access to resources, and that type of
use case.
After applying the NIST AI Risk Management Framework, high-
consequence risks were identified, and particularly that affect
individuals or, again, communities or society, then the
Blueprint for the AI Bill of Rights would be a way to think
through what are the rights of an individual and what are those
issues that need to be addressed.
Senator Rosen. Those are complementary. Thank you. Mr. Ho.
Mr. Ho. Yes, I think it is a really important question.
There are a lot of commonalities in terms of the principles
that the NIST AI Risk Management Framework, the Blueprint for
an AI Bill of Rights, and Executive Order 13960 are trying to
get at.
What I have seen in a number of agencies is a real struggle
of how to actually bring that into practice when agencies are
thinking about piloting, evaluating, and implementing these
kinds of AI use cases. That is why I think what is really
critical, and what I highlighted in my opening remarks, is to
build pathways for technical talent into the public sector. It
will not be possible to really do the kind of red-teaming,
evaluation of these kinds of use cases unless we build on
existing short-term programs like the Presidential Innovation
Fellows (PIF), the GSA, things like the Intergovernment
Personnel Act (IPA) mechanism, but also think about long-term
pathways, things like the U.S. Digital Service Academy, and why
it is so important that OPM actually create the AI hiring line
that was due as of July of last year.
Senator Rosen. You did not really know my next question,
did you, because it really is on the workforce challenges, so
you set me up just perfectly there. Our existing cyber
workforce shortages, we know at Federal agencies they have a
really significant impact on our national security, so the
private sector trying to hire for AI, for cybersecurity,
everything in the technology space. There is really this huge
gap between talent and the jobs that we have to fill, so we
have to continue to invest in our cyber workforce.
I am going to ask both of you again. Dr. Parker, how do you
think we can use AI in the short term to overcome cybersecurity
skills shortages across Federal agencies, knowing that I
believe AI potentiates what humans can do and may help us do
things faster, but in the end humans need to make those
decisions. But what do you think we can do while we are
building the pipeline?
Dr. Parker. I do think that there are some uses of AI, some
applications of AI that are increasing productivity of
individuals. That is true for many different kinds of areas.
You asked about cybersecurity, in particular, or just the cyber
workforce in general. I think being able to use AI, again, in
ways that are somewhat mundane, but there are ways that we can
manage a lot of paperwork and be able to identify ways that we
can more efficiently address the needs of the American
consumer.
I do think it is challenging at the moment to say that we
can use AI as a substitute for people. I think AI is not ready
for that. I think AI is very much more a collaborative tool,
and as a collaborative tool it can help, again, people to work
more efficiently. At the same time, we desperately need to work
on getting more expertise into government.
One quick way of doing that, I think, is to leverage these
programs like the Intergovernmental Personnel Act and the
Presidential Innovation Fellows Program to get people from
industry and from academia into government. I think that is a
very quick way to leverage those programs more, to get more
expertise in government.
Senator Rosen. Thank you. I am over my time, so do you want
me to let him finish or take it for the record later, Mr.
Chairman.
Chairman Peters. Be quick.
Mr. Ho. I will be quick. One estimate has it that we need
40,000 positions in the public sector for cybersecurity. It is
absolutely critical to figure out the pathways of bringing
people in.
I think it is not just a matter of salary scales. As I
mentioned in my opening remarks, the National Security
Commission on AI really highlighted that what is really needed
is also providing opportunities for technical talent to perform
meaningful work within government.
The last thing that I will just say quickly, as to why
really the way I tend to think about it is augmenting the
existing Federal workforce rather than displacing them, is that
you are always going to ultimately need a human in charge.
I will give you one example going back to 1983, when there
was an automated missile detection system that the Union of
Soviet Socialist Republics (USSR) was using, that indicated
that there were multiple missiles being fired. There was one
individual by the name of Petrov who went against strategic
operating protocol because he had a hunch that the system was
malfunctioning. He is often said to be the person who actually
saved us from nuclear war.
We have to have humans in charge to understand the
limitations of these kinds of systems, who know when humans
should override them, in order to really work effectively and
safely with these kinds of systems.
Senator Rosen. Thank you. I could not agree more.
Chairman Peters. Thank you, Senator Rosen.
Senator Padilla, you are recognized for your questions.
OPENING STATEMENT OF SENATOR PADILLA
Senator Padilla. Thank you, Mr. Chair. I wanted to thank
you also for your flexibility. I am back and forth with the
Judiciary subcommittee on AI this morning as well.
Artificial intelligence is already transforming how
government agencies serve the public. Federal agencies are
leveraging AI in a number of important ways, including to
support disaster response and emergency management efforts, to
detect financial fraud and identity theft, to offer chatbots
and virtual assistants that enhance customer service and
engagement, as well as for environmental monitoring and
conservation.
I lay this out because a lot of times we are trying to have
these conversations about AI, the potential, some risks, and
things to keep in mind, as if it is a far-off-into-the-future
dynamic. No, this is a here-and-now conversation.
But clearly there are risks that we will need to address,
especially the sensitive context of providing government
services. Automated decisionmaking systems and tools risk
exacerbating the many existing inequities in our society, as
some of the testimony today reflects. Again, Mr. Chairman,
thank you for calling this hearing. I look forward to hearing
from our panel today on how we can ensure that fostering
trustworthy, equitable, and accountable applications of AI in
government can be achieved.
Now in reviewing the published inventory of AI use across
the Federal Government--again, today, not into the future--I
was pleasantly surprised to see that many agencies are building
AI tools in-house. Some argue that it is more cost effective
for our government to simply purchase AI products developed by
the private sector, whether it is off the shelf or customized.
However, many of the testimonies that we have heard today
explicitly mentioned that government technologists and in-house
tools are important to ensuring that they comply with the
relevant regulations and have the focus of furthering agencies'
mission, not always achieved when you contract out.
Mr. Eppink, first question. In your testimony you
highlighted some of the tensions that arise when government
relies on proprietary private sector tools that are not always
available for public scrutiny. What are some of the factors
that should go into agency decisions to build a tool internally
as opposed to procuring it externally?
Mr. Eppink. Thank you, Senator, for the question, and you
are absolutely right. This is definitely a here-and-now
problem, and we do have these frameworks that have been
discussed today, but what we do not have, at least that I have
observed, is the on-the-ground enforcement and policing of
those, and that is especially true when it comes to these in-
house versus proprietary systems.
This should be an easy question, I think. We cannot allow
proprietary interests--when we are talking about government
making decisions about individuals and their families--to hold
due process rights hostage. To the extent that a government
agency or a State-funded agency needs to use, or wants to use
automated decisionmaking if they have gone through the process,
a transparent process of deciding that that is appropriate in
the first place, if there is going to be proprietary or private
interest providing any of that, the price of admission is going
to have to honor Americans' due process rights, equal
protection rights, which would mean the transparency of those
proprietary systems, so that my clients, for instance, in
Idaho, can access the information about the data, the methods,
and the processes involved so they can evaluate whether they
need to challenge those government decisions, and challenge
them and appeal them if they have to.
Senator Padilla. Thank you. For the follow-up question I
invite Dr. Parker and Professor Ho to chime in. What are some
of the short-term steps Congress can take to help agencies hire
and retain technical talent in this area? We heard from Senator
Rosen about the field, in general, I think more in the private
sector the need for some of the workforce challenges in this
space. But on the government side, in particular.
Dr. Parker. Thank you for that question. I think there are
a number of barriers right now to having that type of expertise
in government. One of the barriers, I think, is a salary
barrier, and an understanding of the kinds of skills and
knowledge that are needed in order to fill a particular AI
role. I think if the occupational series that OPM is working on
were developed, it would help us to identify what those skills
and roles and knowledge are that are needed for certain jobs in
AI in the Federal Government, and that would give us more
ability to reach out to people that have those kinds of skills
and to train to those skills through things like boot camps.
A few years ago there was a boot camp in cybersecurity that
brought in people from across the Federal Government who were
interested in learning cybersecurity skills but did not yet
have them. The Chief Information Officers (CIOs) got together
and trained these people, and they now have those additional
cybersecurity skills. We could do something similar in AI.
The challenge is always how to scale it. Congress could
fund these kinds of boot camps so that we could use the current
workforce that we have but to provide those new skills and
knowledge that is needed to participate in the AI space.
Senator Padilla. Professor Ho, anything unique to add,
because I have an additional question on a different topic for
you as well.
Mr. Ho. I will just say, quickly, as I see on this side of
the coast, Senator, I will say quickly the other part is to
build on Science, technology, engineering, and mathematics
(STEM) education. I think for the longest period of time the
United States has just been a magnet for top scientific and
technical talent. But increasingly there are international
students that are choosing to leave the country. The country of
Canada, for instance, has had very specialized programs to
actually attract top AI talent into the country, and that would
be another mechanism really to ensure that we maintain U.S.
leadership in AI.
Senator Padilla. You frame it as many graduate students
choosing to leave the country. In too many instances there is
no choice, given our immigration system, the need for
modernization, but that may be a topic for another day.
Professor Ho, I mentioned another topic I wanted to make
sure to ask a question on. In the paper that co-authored on the
use of artificial intelligence in Federal administrative
agencies, you found that law enforcement applications were the
most common use case for Federal agencies that adopted AI
systems.
As you talk about building trust and ensuring
accountability for automated decisionmaking, law enforcement
activity is often the area in which the public has the least
amount of insight, let alone oversight, yet it is also the area
where government decisionmaking, including tools used to guide
that decisionmaking, have tremendous consequences for the
public, particularly for historically marginalized communities.
Professor Ho, can you speak to the unique challenges of
guarding against bias and ensuring accountability and equity in
the use of AI by law enforcement?
Mr. Ho. Thank you, Senator, for that question, and thank
you for noting the report that we conducted a number of years
ago. I should clarify that in that report we looked largely at
the use of AI by civil agencies, and one of the challenges has
actually been exactly to get transparency within the criminal
justice system. That is reflected, as well, in some of the
exemptions under, for instance, the AI Bill of Rights or the
scope of requirements to file AI use case inventories. That is
why I think these AI use case inventories are such a kind of
critical tool of transparency. You cannot manage what you
cannot measure, and so I think AI use case inventories are an
important first step in that regard.
Let me loop back, though, to the earlier question that you
had, just in terms of accountability, and give you one example
of the sort of enforcement context that I think is very telling
that connects this to the necessity to have human talent within
government. Within the Securities and Exchange Commission (SEC)
there was an internal team that built out a series of very
innovative ways to scan filings, to look at the risk, for
instance, of insider trading based on what folks had filed to
the SEC, using natural language processing.
But what was very important in that use case was that it
was internally developed first, and it was the interaction
between the technical engineers and the line-level prosecutors
that really ensured accountability. The line-level prosecutors
within the SEC said, ``I am not persuaded by your risk score.
You need to explain to me why this system is actually
identifying this case as something that I should prosecute,
because ultimately I need to bring this in front of a judge.''
That is exactly the reason why technical talent and the ability
of technical talent to work with the domain experts is going to
be so critical to have these kinds of forms of internal
accountability for AI systems.
Senator Padilla. Thank you very much, and Mr. Chair, if I
could just add one last note, not another question but just one
last note. Tapping into my prior experience as Secretary of
State of California, where we introduced the first chatbot in
California State government to assist people navigating our
website, also automating how business owners and entrepreneurs
could file their necessary paperwork with the State of
California, there is, at times, a personnel concern that
through additional automation and efficiency that we do not
need as many workers, and therefore it is a mechanism toward
layoffs and staff reductions.
What we were able to do instead was actually free up
people's valuable time from, ``pushing paper'' to improve
customer service and dedicate that experienced frontline
personnel to some of the more complex questions or
troubleshooting that a lot of either individuals or customers
of government sometimes have frustrations with, long wait
times, et cetera. There can indeed be that win-win.
Thank you, Mr. Chair.
Chairman Peters. Thank you, Senator Padilla.
Senator Ossoff, you are recognized for your questions.
OPENING STATEMENT OF SENATOR OSSOFF
Senator Ossoff. Thank you, Mr. Chairman, and thank you to
our panelists for joining us today.
Some of these questions I think will get at implementation
within government and some will also touch upon how we think
about potential proposals for broader regulation of the
technology, so please bear with me as we move through the
discussion.
First is about definitions, and maybe Dr. Parker, I could
start with you, and I would also like to hear from Professor
Ho. But as these technologies become more ubiquitous and
modular and they are incorporated in software suites, and they
are plugged in as tools for various purposes throughout an IT
infrastructure, how, both in terms of administering within
government, their deployment, but also thinking about broader
regulation do we fundamentally define what it is that we are
regulating? As concisely as you can, how would you define the
scope of the technologies that are the subject of our interests
and require additional scrutiny?
Dr. Parker. Certainly the definition is something that
people do not agree on, in general. I like to focus on use
cases and how the use of a technology is important in impacting
the particular application domain.
I look at it in terms of systems that are typically data
driven, they often learn over time and change their behavior
over time, and they are often doing tasks that we frequently
have attributed to require human intelligence in the past. That
is my succinct definition.
Senator Ossoff. OK. Thank you, Dr. Parker. Professor Ho,
again, imagine you are legislative counsel and you are drafting
legislative text, and your purpose is to define the technology
subject to certain regulations. What text would you propose?
Mr. Ho. I very much agree with Dr. Parker that the focus
should be on use cases. I think a lot of regulations refer back
to the National Defense Authorization Act (NDAA) definition,
which is a relatively expansive one. One way to handle this, I
actually think it would be to have further guidance and
clarification coming out of the relevant offices that are
working with agencies. For instance, the inconsistency in how
AI use case inventories have been handled steps exactly from
your question.
Senator Ossoff. Is it about use case or is it about
capability? The use case is going to be almost universal, I
think, within a couple of years. Is it the purpose for which it
is being used that defines whether it constitutes the regulated
technology, if we are thinking about regulation, or is it the
capability of the technology as the qualities of the software?
Mr. Ho. I think, definitionally, we can go with the
capabilities of the software, and that is very much, I think, I
would agree with Dr. Parker that the kind of focus typically
has been on machine learning systems that are able to learn and
predict in the way that really simulates what humans could do.
But I think when it comes to the actual regulation, which I
think you are also trying to get it, it is really important to
look at the particular use cases to identify what kind of risks
are being posed.
Senator Ossoff. All right. Let us think about some of the
constitutional and other questions that may arise. Mr. Eppink,
from your standpoint, thinking about, for example, evidentiary
predicate that justifies certain police actions or can be used
to secure certain court orders. One of the things that I have
been grappling with is given the massive datasets that are
available in open source or that can be purchased by a state
actor, the capacity for predictive behavioral modeling is
potentially very significant. Perhaps with a high degree of
integrity some of this technology will be able to assign
probabilities with respect to future conduct by individuals.
The risk here, I think, is that prosecutors or law enforcement
agencies may use such predictive modeling in order to justify
forms of surveillance or to seek warrants or to take other
action.
How do you think about that risk and how should Congress
think about that risk?
Mr. Eppink. Thank you, Senator. Yes, especially based on
what we have heard today, we have, I think, at best, principles
to start to think about how to build these systems in a safe
way, in a democratic way, but I do not believe they are being
built that way right now. Especially when we are talking about
law enforcement and the risk of life and liberty, the
technology may be developing but the governance of that
technology is not yet there to be condoning use of AI in those
contexts. I am not even sure, based on what we have seen in
Idaho in the Medicaid context of comparatively very rudimentary
automated system, that we are there yet, and I think the
necessity of litigation there corroborates that.
We have to go beyond these jumping-off points that we have
been discussing today, create clear governance, and include the
people who these systems will be making decisions about in the
process of selecting whether there will be automated
decisionmaking in the first place and how those things will be
crafted to ensure that the systems are built fairly and used
fairly.
Senator Ossoff. Dr. Parker, how do you think about the due
process concerns, and those could be both in a criminal context
or in a more seemingly mundane administrative context, like
eligibility for certain forms of aid?
Dr. Parker. I think this is a complex question, Senator,
and thank you for that.
Senator Ossoff. You have one minute and nine seconds.
Dr. Parker. I am not going to answer it all. I am going to
suggest that one way to move forward is for the Subcommittee on
AI in Law Enforcement that was directed by Congress in the
National AI Initiative Act, to actually be established. This is
something that is a subcommittee of the National AI Advisory
Committee, and that would be experts from across the different
sectors of interest, whether it be private sector or----
Senator Ossoff. OK. So more study needed.
Dr. Parker [continuing]. A group of experts that can enable
us to understand this more deeply.
Senator Ossoff. OK. Big subject, seven minutes.
My final question for you, Dr. Parker--and you have
probably thought about this a lot, now and in your past roles--
in government there is a lot of focus on insider threats,
whether in an intelligence context or otherwise. When we are
thinking about autonomous actors, or actors who have a measure
of autonomy, within public agencies, who are not humans, how do
we think about the risk of cooptation, manipulation,
exploitation--there is cybersecurity aspect here. There is a
software design aspect here--that these tools themselves could
pose certain insider threats for unauthorized disclosure or for
using their network accesses to enable penetration of
government systems, or otherwise.
Dr. Parker. I think there is a question of what could be
and what is true today. I do not work in the national security
space myself, but I think what we can do with AI systems right
now are there are technologies, if you are thinking about a
human insider threat, there are technologies that can actually
track behaviors and determine whether or not people are doing
what they should be doing in the intelligence system space. I
am talking about inside the trusted people. There are those
kinds of technologies that can help us protect against the
human insider threat.
But then there is also the bigger question of down the road
if these more general AI systems have the capability to dig
into our systems and what we do about that. That is not
something that I think is the here and now. I think it is
something that we need to think ahead on. But I do not believe
those systems exist today.
Senator Ossoff. Mr. Chairman, could I ask one more
question? I am over time.
OK, please, as succinctly as you can, Dr. Parker, and then
Mr. Eppink, if there were three State actions, things that
governments do, where, referencing what Professor Ho said
earlier, we not only must ensure that humans are in the loop,
but let us say we wanted to take a provisional decision that at
this time no such action should be permitted to be even
influenced by this kind of technology. Like for example,
decisions about when to use lethal force, decisions about when
to conduct surveillance.
Are there certain governmental functions that given the
level of risk right now you would nominate to rule out as being
supplemented or guided or supported by these technologies, or
are there not?
Dr. Parker. The launching of nuclear weapons, for instance.
I think DOD is overseeing a number of ethical uses of AI, but
there are some cases like that, that I think strategically
cannot be allowed to happen. That would be my No. 1 on the
list.
Senator Ossoff. Mr. Eppink.
Mr. Eppink. Yes, I think we can look to the Constitution.
We can look to life, liberty, property, privacy. To the extent
that we have systems that are involved in making decisions on
those things that affect individuals and families, we have to
ensure that there is the transparency, the inclusion, the
reliability, the independent auditing and testing of those
programs before they should be deployed in government uses.
I think we can get there, in some instances, but we are not
there now, and especially when you were talking about making
decisions about use of lethal force or other decisions that
could take a life or threaten a life. I think there is
governance that is not yet in place to ensure those systems are
fair.
Senator Ossoff. Thank you. Thank you, Mr. Chairman.
Chairman Peters. Thank you, Senator Ossoff.
We are ready to wrap up a great hearing. We have covered an
awful lot of ground, a big topic. I would like to wrap up these
kinds of hearings with just asking folks to kind of focus one
of the issues.
I am going to be asking everybody on the panel--and I am
going to start with you, Mr. Siegel, and then we will work down
the panel--so in terms of AI policy going forward, what would
you say, from a legislative standpoint, because we are
legislators here, in Congress, in terms of legislating, what
would be the No. 1 item that we should prioritize in terms of
thinking about future legislation?
I am going to start with you, Mr. Siegel. All of you have a
lot of different issues that you are working with, but No. 1,
for legislating. There are other things that we could be doing,
regulations and other kinds of policies, or norms, et cetera,
but from a legislative standpoint is there something that
really stands out to each of you?
Mr. Siegel.
Mr. Siegel. Understood, Mr. Chairman. Transparency would be
the No. 1 issue from a legislative point of view, enforcing
transparency in the use of AI. I think that if you were to
focus legislatively on transparency that would take care of a
host of attendant issues such as collusion between the
government and corporate sector, privacy issues, even
potentially pointing the way toward some kind of private data
ownership so that the uses of people's data was something that
if they were not at least initially financially staked into,
they could at least have some visibility on.
Transparency, I think, is where to begin, and from there
that would lead into other related matters.
Chairman Peters. Thank you, Mr. Siegel. Mr. Ho.
Mr. Ho. Chairman, a number of months ago, Eric Schmidt was
asked about how Congress had implemented the recommendations
from the National Security Commission on AI, and he was
generally quite favorable. But there was one thing he singled
out, which is getting technical talent into the Federal
workforce, which is the predicate for, I think, addressing a
range of these issues that we have talked about today.
Take procurement. I think the AI Training Act is fantastic.
But what I have seen at a number of agencies is that
procurement officials say, ``It is the business unit within the
agency that has to make this decision,'' and the business unit
says, ``It is really up to the procurement official.'' I think
what we really need is the kind of blended expertise that
brings domain expertise and technical talent together to really
be able to protect American values.
Chairman Peters. Thank you. Dr. Parker.
Dr. Parker. Thank you, Mr. Chairman. I would say that we
are suffering right now from a lack of leadership and a lack of
prioritization on these topics. One, I think, quick way,
legislatively, that could be done to address this is to appoint
those Chief AI Officers at each agency, where they are given
the responsibility and the resources to oversee the uses of AI
and to develop strategies for their use of AI within their
agencies, with accountability for delivering those and updating
them regularly.
To establish a coordination body, like a Chief AI Officers
Council, that is responsible for coordinating these activities
across all the Federal Government, with an intent of
prioritizing this activity across the Federal Government,
providing that leadership as well. Then they can also work on
helping with the workforce issues, I believe, within their
agencies, by identifying what the needs are and putting those
priorities on getting that workforce into the Federal
Government.
Chairman Peters. Thank you. Mr. Ariga.
Mr. Ariga. Thank you, Chairman. My No. 1 priority, and you
might indulge me in the hyphenated response, one is on
disclosure where discretionary decisionmaking is being
impacted. The use of it, the process, and any redress. But
fundamentally it is the digital-ready workforce that will make
such disclosure effective. I will echo with Dr. Ho's
recommendation in terms of developing a Federal digital-ready
workforce.
Chairman Peters. Thank you. Mr. Eppink.
Mr. Eppink. Thank you, Chairman. The experts on these
systems are the people that the systems make decisions about. I
worked on this litigation in Idaho for 12 years now, almost,
and I have worked with agency officials, I have communicated
with Federal overseers, I have communicated with the courts.
But it is time and time again my clients who have been able to
spot the most important systemic problems with these systems.
I appreciate the opportunity to testify to the Committee
today. The people that the Committee especially should hear
from and the policies that the Congress should put in place
should make sure that at each step in the development and
selection of these systems, the people who they make decisions
about are included it. My clients, people with disabilities in
Idaho, often use the phrase, ``nothing about us, without us,''
and that is critical in these automated decisionmaking systems
and AI systems. Thank you.
Chairman Peters. Tank you. I would certainly like to thank
each of our witnesses for being here today, and I think, the
entire Committee is grateful for your expertise and for your
willingness to come forward to answer our questions.
I think as we heard today the use of automated systems to
help government provide public service more efficiently is not
new. However, as we enter the age of rapid development of
advanced machine learning methods and other forms of artificial
intelligence now--not waiting, now--is going to be the time to
ensure that these systems that the government is using and will
procure for use in the future do not have unintended, harmful
consequences.
It is critical the Federal Government act quickly to set
appropriate guardrails and oversight policies that protect the
public. I think we all agree that Americans deserve a
government that is modern, efficient, innovative, as well as
one that is transparency, fair, trustworthy, and protects their
privacy. As Chairman of this Committee I am going to continue
to work to ensure that the government lives up to those key
principles, and your testimony today will help inform the
Committee's future legislative activities and oversight actions
in the years ahead.
The record for this hearing will remain open for 15 days,
until 5 p.m. on May 31, 2023, for the submission of statements
and questions for the record.
This hearing is now adjourned.
[Whereupon, at 11:54 a.m., the hearing was adjourned.]
A P P E N D I X
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