    [Senate Hearing 118-545]
    [From the U.S. Government Publishing Office]

                                                           S. Hrg. 118-545

                         AI IN CRIMINAL INVESTIGATIONS
                                AND PROSECUTIONS

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

                                    HEARING

                                   BEFORE THE

                         SUBCOMMITTEE ON CRIMINAL JUSTICE
                                 AND COUNTERTERRORISM

                                     OF THE

                           COMMITTEE ON THE JUDICIARY
                              UNITED STATES SENATE

                        ONE HUNDRED EIGHTEENTH CONGRESS

                                 SECOND SESSION

                                   __________

                                JANUARY 24, 2024

                                   __________

                               Serial No. J-118-52

                                   __________

             Printed for the use of the Committee on the Judiciary

    [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

                            www.judiciary.senate.gov
                                www.govinfo.gov

                                    __________

                       U.S. GOVERNMENT PUBLISHING OFFICE
    58-370                     WASHINGTON : 2025

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

                           COMMITTEE ON THE JUDICIARY

                       RICHARD J. DURBIN, Illinois, Chair
    SHELDON WHITEHOUSE, Rhode Island     LINDSEY O. GRAHAM, South Carolina,
    AMY KLOBUCHAR, Minnesota                 Ranking Member
    CHRISTOPHER A. COONS, Delaware       CHARLES E. GRASSLEY, Iowa
    RICHARD BLUMENTHAL, Connecticut      JOHN CORNYN, Texas
    MAZIE K. HIRONO, Hawaii              MICHAEL S. LEE, Utah
    CORY A. BOOKER, New Jersey           TED CRUZ, Texas
    ALEX PADILLA, California             JOSH HAWLEY, Missouri
    JON OSSOFF, Georgia                  TOM COTTON, Arkansas
    PETER WELCH, Vermont                 JOHN KENNEDY, Louisiana
    LAPHONZA BUTLER, California          THOM TILLIS, North Carolina
                                         MARSHA BLACKBURN, Tennessee
                     Joseph Zogby, Majority Staff Director
                    Katherine Nikas, Minority Staff Director

             Subcommittee on Criminal Justice and Counterterrorism

                       CORY A. BOOKER, New Jersey, Chair
    SHELDON WHITEHOUSE, Rhode Island     TOM COTTON, Arkansas, Ranking
    AMY KLOBUCHAR, Minnesota                 Member
    CHRISTOPHER A. COONS, Delaware       CHARLES E. GRASSLEY, Iowa
    PADILLA, ALEX, California            JOHN CORNYN, Texas
    OSSOFF, JON, Georgia                 MICHAEL S. LEE, Utah
    LAPHONZA BUTLER, California          TED CRUZ, Texas
                                         JOHN KENNEDY, Louisiana
                     Lynda Garcia, Democratic Chief Counsel
                     Drew Hudson, Republican Chief Counsel

                                C O N T E N T S

                                  ----------

                               OPENING STATEMENTS

                                                                       Page

    Booker, Hon. Cory A..............................................     1
        Prepared statement...........................................    32
    Cotton, Hon. Tom.................................................     2

                                   WITNESSES

    Aguilar, Armando.................................................     5
        Prepared statement...........................................    35
        Responses to written questions...............................    61
    Howard, Karen L..................................................     7
        Prepared statement...........................................    38
        Responses to written questions...............................    63
    Wexler, Rebecca..................................................     9
        Prepared statement...........................................    54
        Questions submitted with no response returned................    64

                                    APPENDIX

    Items submitted for the record...................................    31

                         AI IN CRIMINAL INVESTIGATIONS
                                AND PROSECUTIONS

                                  ----------

                          WEDNESDAY, JANUARY 24, 2024

                          United States Senate,
                  Subcommittee on Criminal Justice and
                                      Counterterrorism,
                                    Committee on the Judiciary,
                                                        Washington, DC.
        The Subcommittee met, pursuant to notice, at 2:32 p.m., in
    Room 226, Dirksen Senate Office Building, Hon. Cory A. Booker,
    Chair of the Subcommittee, presiding.
        Present: Senators Booker [presiding], Whitehouse, Padilla,
    Ossoff, Butler, Cotton, and Kennedy.

               OPENING STATEMENT OF HON. CORY A. BOOKER,
              A U.S. SENATOR FROM THE STATE OF NEW JERSEY

        Chair Booker. Hello, everybody. I'm excited to see such a
    crowd, almost standing room only. There's a whole bunch of
    people waiting outside. I'm not sure if it was because of the
    rumors that broke out on Twitter that you and I were going to
    have a cage match.
        But in all seriousness, I can't tell you how excited I am
    being next to Tom Cotton for this hearing because I think as
    much as Tom Cotton and I have that differs us, we actually both
    have a lot of the same commitment to community safety. And I
    think this is a hearing that strikes right to the heart of
    keeping communities safe and strong. And I'm excited that the
    two of us are here for this inquiry, really to hear from folks
    about an issue that is really exciting.
        There is much made of technology and its influence on our
    society. When I was mayor of the city of Newark, the number one
    issue of my constituents, the pollster that I had and never
    seen anything like it, was the safety and the security of our
    neighborhoods and communities. And what was exciting for me is
    that we introduced a lot of technology as a mechanism with
    which to keep people safe, technology now that some of our
    witnesses actually can talk to, like ShotSpotters, and cameras,
    license plate readers, and more.
        And what excites me now is I was a mayor about 11 years
    ago, which is a long time when it comes to the advancements of
    technology and innovation. We know that technology in America
    has so much promise, and in this sphere, it could do a lot.
    Many people will be stunned to know that our murder clearance
    rates in the United States is--something where, Chief Aguilar,
    you can help me out--but somewhere around 50 percent.
        Mr. Aguilar. Correct.
        Chair Booker. Yeah. It's astonishingly--I think that was a
    hallelujah that he said there. Amen, Senator Booker--is
    astonishingly low. And so, one of the things that a lot of
    people don't realize is that technology could help us on
    clearance rates. It could help us to create more community
    trust. It could help us on investigations. It could help us in
    so many positive ways.
        But we also know that technology presents a lot of
    possibilities to undermine our core values and our ideals as
    well when it comes to certain constitutional protections, when
    it comes to privacy, when it comes to some of the most
    sacrosanct elements of our democracy. And so we know that AI is
    consequential in our society as a whole in ways that many of us
    can't fully imagine, both in the possibility in the promise, as
    well as the potential problems.
        When it comes to our criminal justice system, where often a
    person's liberty and a person's life are at stake, when the
    safety of their neighborhoods are at stake, where their
    constitutional protections are at stake is particularly
    consequential. Law enforcement's use of artificial intelligence
    technology is not a recent development, again, as all of our
    experts can attest to.
        Its recent expansion raises a lot of questions, as well as
    raises a lot of excitement for me about the possibilities. And
    so, I'm going to submit the rest of my opening testimony for
    the record.
        [The prepared statement of Chair Booker appears as a
    submission for the record.]
        Chair Booker. But I do want to say to the witnesses that
    are here, how excited I am. I know you all made sacrifices and
    took time out. I'm going to introduce you in a moment after my
    Ranking Member, Senator Cotton, does this opening statement.
        But we really are at a point in humanity where every
    generation has breakthrough technologies that shape and alter
    the course of humanity. AI is most certainly one of those
    things, and I know that Senator Cotton and I both are committed
    to trying to find a way to capture the possibility as well as
    protect against the risks.
        And with that I'm very excited and very grateful to Senator
    Cotton and his entire team for helping to make this hearing
    possible. And I will turn the microphone over to him for his
    opening remarks.

                 OPENING STATEMENT OF HON. TOM COTTON,
               A U.S. SENATOR FROM THE STATE OF ARKANSAS

        Senator Cotton. Thank you, Senator Booker. I would say that
    artificial intelligence has gained a lot of attention lately,
    over the last year and a half or so in particular, but the core
    technology has been around for some time because of how it's
    depicted.
        Maybe we start with what it's not. Many people,
    fortunately, most people, don't have much personal interaction
    with law enforcement or with the criminal justice system. What
    is AI in law enforcement? It's not RoboCop, it's not the
    Terminator, it's not the Matrix, not Ultron, it's not even
    WALL-E. It is never used independent of human decisionmaking in
    our criminal justice system. That's why it's a tool in the law
    enforcement toolbox, and it can provide impressive time-saving,
    and crime-solving, and justice-serving tools.
        Just one example. Facial recognition software, for
    instance, could take a crime scene sketch or a security camera
    image, and go through thousands and thousands of pictures in a
    public data base, say a driver's license data base. It can
    eliminate obvious non-matches. It might be even narrow it down,
    but then that is a tool that independent human judgment can use
    to pursue leads. If the early stages of a criminal
    investigation are like looking for a needle in a haystack, then
    artificial intelligence, if you will, can provide a magnet for
    some of our criminal investigators in the police forces around
    the country.
        That's why I want to enter into the record with consent
    here, a statement from the National Sheriff's Association,
    Major County Sheriffs of America, and the Major Cities Chief
    Association. They represent State and local law enforcement all
    across the country. I want to highlight one particular line
    from their statement. ``It is essential to recognize that AI-
    powered technology serves as an investigative assistant to law
    enforcement rather than a replacement for the human element.''
    Mr. Chairman, I ask consent to enter that statement into the
    record.
        Chair Booker. Without objection.
        Senator Cotton. I also want to give one concrete real-world
    example of how this can work. Last summer, British authorities
    contacted the Department of Homeland Security about a video,
    child sexual exploitation. They had reason to believe it was
    made in the United States. DHS ran the faces against a mass
    data base of photos, and they found a match. A college sports
    administrator in Missouri.
        Investigators then found the suspect's Facebook profile and
    were able to confirm not only that it was the same person, but
    even that the child victim was on his Facebook page. They
    eventually got a search warrant from a judge, and in July, they
    were able to arrest and charge this predator. Without that
    facial recognition technology, he might be free today, and that
    child might still be subjected to abuse today as well.
        AI-powered law enforcement tools also improve efficiency
    and save lives in other ways. For instance, by modeling where
    and when crimes happen, law enforcement agencies can better
    position limited patrol units to prevent crimes or to respond
    quickly. They can coordinate with other emergency services like
    ambulances for better staffing at times and places where there
    would be needed.
        And yet other cases, AI-powered products like those made by
    Motorola Solutions are used by 911 call centers to clean up,
    transcribe, and even translate 911 calls in real time, or an AI
    product made by the company, Axon, is used by law enforcement
    to quickly blur faces and body cam footage, so police
    departments can share the footage with the public faster. Other
    AI-powered tools can analyze financial records to help identify
    financial crime, and AI-powered cameras can recognize license
    plates of wanted individuals and alert law enforcement to
    investigate.
        Now, I understand there are some who have concerns about
    the use of artificial intelligence technology in law
    enforcement, and those concerns are in some cases valid and
    should be aired, which I think we'll do today. But I do want to
    point out that probably the most uncomfortable people with the
    use of artificial intelligence are criminals who would like to
    avoid being caught because, again, artificial intelligence is
    simply a tool that human investigators, police officers,
    prosecutors, can use to help solve cases, convict criminals,
    and put them behind bars.
        We need to remember that artificial intelligence-powered
    law enforcement tools are assistive technologies helping law
    enforcement officers be better at their jobs, and responsibly
    used, these technologies can help create a faster, cheaper,
    more accurate criminal justice system where the criminals are
    being caught, prosecuted, and victims are being provided
    justice, and the innocent are not being punished.
        I know our witnesses have thoughts and expertise to share
    on these questions, and I look forward to today's conversation.
    I do want to say and apologize in advance that since this
    hearing was scheduled, the Republican Conference has scheduled
    an all-Republican meeting about the National Security
    Supplemental Bill that is currently under debate. So, I may not
    make it to the end, but I assure you it has nothing to do with
    your testimony. Thank you-all.
        Chair Booker. Senator Cotton, thank you for those excellent
    remarks. You went through an array of movies there from WALL-E
    to Terminator. Were those recommendations, or did your staff
    sort of put them in?
        [Laughter.]
        Chair Booker. All right. I am excited that what I'm going
    to do now is introduce all the witnesses, and then I'm going to
    ask you to stand up, and raise your right hand, and swear an
    oath. We prefer you not to swear at us, but just swear the
    oath, and then we'll go and open it up for your testimony in
    alphabetical order.
        So, I'm going to introduce really quickly the Assistant
    Chief of Police, Armando Aguilar, from the city of Miami
    Department and Law Enforcement Subcommittee Member of the
    National Artificial Intelligence Advisory Committee. Assistant
    Chief Aguilar is assistant chief of police for the city of
    Miami Police Department, which is having some extraordinary
    success in protecting citizens and keeping their community
    safe.
        In 2022, you began serving as a member of the Law
    Enforcement Subcommittee of the National Artificial
    Intelligence Advisory Committee, which advises the President of
    the United States on matters related to the use of AI in law
    enforcement. As assistant chief of police, you oversee hundreds
    of sworn and civilian employees, and you've held many senior
    management positions in all divisions of the Miami Police
    Department.
        During your time with MPD, you have implemented offender-
    focused strategies that have contributed to significant
    reductions in violent crime and significant increases in
    clearance rates. You hold a master in public administration
    from Barry University, and a bachelor's of criminal justice
    from Saint Leo University. It's really an honor to have you
    here, and we're very, very grateful.
        I want to next introduce Dr. Karen Howard, Ph.D. Dr. Howard
    works with the U.S. Government Accountability Office, or GAO,
    as acting chief scientist, a role that she held from Jan.-Oct.
    2023, and a director of the GAO Science, Technology Assessment,
    and Analytics Team, STAA. You manage a broad portfolio of
    topics, frankly, an impressive portfolio of topics, including
    forensic algorithms, biological threats, chemical weapons,
    national security implications of emerging technologies, and
    other issues. It's pretty extraordinary the work you do.
        You've produced key GAO reports that you're going to
    testify about, including forensic technology algorithms used in
    Federal law enforcement, and Forensic Technology: Algorithms
    Strengthen Forensic Analysis, but Several Factors Can Affect
    Outcomes. You've earned your Ph.D. in environmental chemistry
    and your master's degree in analytical chemistry. You are, as
    my dad would say, you have more degrees than the month of July,
    and it's great to have you here. And thank you for, again,
    flying up from Alabama as well.
        It's difficult for me, Professor Wexler, you have so much
    Berkeley on your resume. As a Stanford man, I'm going to try to
    muscle my way through this. Okay. You are assistant professor
    of law at the University of Berkeley School of Law and faculty
    co-director. You're the co-director for the Berkeley Center of
    Law and Technology.
        Professor Wexler is a faculty co-director of the Berkeley
    Center for Law and Technology, and assistant professor of law
    at UC, Berkeley. Your teaching and research focus on data
    technology and secrecy in the criminal legal system. Your
    scholarship has appeared in so many places; the Harvard Law
    Review, Stanford Law Review, Yale Law Journal Forum, NYU Law
    Review, UCLA Law Review, Texas Law Review, Vanderbilt Law
    Review, Berkeley Technology Law Review. I would criticize you
    because nothing in Arkansas. You need to correct for that.
        Professor Wexler served as Senior Policy Advisor for
    Science and Justice at the White House Office of Science and
    Technology during the spring of 2023, and as the James C.
    Carpenter visiting professor at Columbia Law School. You've got
    your JD from Yale, MPhil from Cambridge as a Gates Scholar, and
    you are summa cum laude at Harvard. And again, we thank you for
    flying in all the way from California. Would the three
    witnesses please stand up and raise your right hand?
        [Witnesses are sworn in.]
        Chair Booker. Let the record show that each witness has
    answered in the affirmative. You may sit down, unless you want
    to stand for the hearing, which of course we have no
    preference. You each will now have 5 minutes for your opening
    statement. And again, we're going to start with an alphabetical
    order with Chief Aguilar.

     STATEMENT OF MR. ARMANDO AGUILAR, ASSISTANT CHIEF OF POLICE,
                MIAMI POLICE DEPARTMENT, MIAMI, FLORIDA

        Mr. Aguilar. Good afternoon, Subcommittee Chair Booker,
    Ranking Member Cotton, and Subcommittee Members. I'm Armando
    Aguilar, assistant chief of police at the Miami Police
    Department, also currently serving a 3-year term as a member of
    the Law Enforcement Subcommittee of the National Artificial
    Intelligence Advisor Committee, or NAIAC-LE. I would, however,
    like to point out that I'm speaking here today on behalf of the
    Miami Police Department and not on my position on NAIAC-LE.
        I'm proud to say that the Miami Police Department story is
    among the greatest turnaround stories in law enforcement. In
    1980, Miami with a murder rate comparable to that of Honduras,
    was America's murder capital. I became a Miami police officer
    in 2001, and a homicide detective in late 2004; a year when 69
    people would be murdered in Miami and another 6,400 would fall
    victim to violent crime.
        By this time, we had the audacity to high-five each other
    because we were no longer in the top five most violent cities
    in America. We were, however, perennially on the top 25 list.
    Fast-forward to 2023, Miami ended the year with 31 murders and
    2,600 violent crimes. Thirty one and 2,600 too many still.
        Our murder clearance rate, the rate at which cases are
    solved, was 68 percent, or 97 percent under the FBI's Legacy
    Reporting System, which counts prior year cases closed during
    2023. Our violent crime clearance rate was 58 percent. Now, for
    most of my career, our murder clearance rate hovered below 45
    percent, and our violent crime clearance rate below 38 percent.
    So, what changed? A great deal.
        I'll begin by stating that I've had the pleasure of leading
    the best generation of officers, detectives, and professional
    staff to serve the people of Miami, and our success begins with
    community trust. Violent crime, especially unsolved violent
    crime, is among the greatest threats which serve to undermine
    that trust. A shooting takes place, a community member calls in
    an anonymous tip. The police without any other leads to
    corroborate the tip, will eventually result in the case going
    cold. People stop reporting gunfire, the police in turn do not
    respond to gunfire that we don't know about.
        The perception quickly becomes that the police are, at
    best, unable to keep them safe, or at worst, unwilling to. The
    Miami Police Department has successfully leveraged artificial
    intelligence over the past few years to great effect. We use
    gunshot detection systems, public safety cameras, facial
    recognition, or FR, video analytics, license plate readers,
    social media threat monitoring, and mobile data forensics. We
    use ballistic evidence to connect the dots between shootings
    and the violent actors that are victimizing our communities.
        A recent BJA-funded study by Florida International
    University, found that violent crimes are one such resource was
    used by our detectives, had a 66 percent greater likelihood of
    being solved when compared to similar cases where no such
    resource was used.
        I'm happy to discuss any of the technologies that we
    employ, but I'm going to take this time to discuss how we came
    to develop our policy governing the use of FR in criminal
    investigations. It started in January 2020, when The New York
    Times ran an article by Kashmir Hill. The article was critical
    of the use of law enforcement facial recognition, and of one
    vendor in particular.
        Ms. Hill posed several questions which resonated with me as
    I do spend my time out of uniform as a private citizen. Without
    proper safeguards, Ms. Hill asked, what would stop police from
    using FR to identify peaceful protest organizers, or
    cyberstalking an attractive stranger at a cafe? What about the
    public whose biometric data that is our faces would be analyzed
    by police?
        So my team and I set out to establish a facial recognition
    policy that would address these and other concerns. We're not
    the first law enforcement agency to use facial recognition or
    to develop FR policy, but we were the first to be this
    transparent about it. We did not seek to impose our policy on
    the public, we asked them to help us write it.
        We started out by meeting with local privacy advocates.
    They absolutely hated it, but we wanted to know why they hated
    it. So they told us. We found that many of their critiques were
    thoughtful and reasonable. So we heard their objections, took
    it upon ourselves to treat them as recommendations, and
    incorporated several of them into our policy.
        We highlighted successful arrests aided by FR through local
    media coverage, and that March, we held two virtual town hall
    style meetings virtually because in-person meetings were not an
    option due to the pandemic. One session was conducted in
    English, the other in Spanish. Each session included public
    questions and comments, and each session had about 1,300 live
    views in 3,600 total views.
        The policy that resulted from our effort created a narrow
    framework within which we would use FR. Most importantly, our
    policy emphasizes that FR matches do not constitute probable
    cause to arrest. Matches are treated like an anonymous tip,
    which must be corroborated by physical testimony or
    circumstantial evidence. We laid out five allowable uses;
    criminal investigations, internal affairs investigations,
    identifying cognitively impaired persons, deceased persons, and
    lawfully detained persons.
        We use FR retrospectively, that is, we don't use it in a
    live or real-time basis to identify persons going about their
    business in public spaces. And we do not use it to identify
    persons who are carrying out constitutionally protected
    activities.
        We establish a policy limiting who has access to our FR
    platforms, and we disclose our FR use to defense counsel in
    criminal cases. We do not substantively manipulate or alter
    probe photographs, use composite sketches as probe photographs,
    or use any other technique which has not been scientifically
    validated.
        These efforts, along with many others, resulted in a Miami
    that is safer today than in any other time in our history. I
    thank you for inviting me to speak before the Subcommittee, and
    I'm happy to answer any questions that you may have.
        [The prepared statement of Mr. Aguilar appears as a
    submission for the record.]
        Chair Booker. Thank you. That's extraordinary testimony.
        I remind the witnesses that Senator Whitehouse and Senator
    Kennedy are here, so you should be on your best behavior. Dr.
    Howard.

              STATEMENT OF MS. KAREN L. HOWARD, DIRECTOR;

             SCIENCE, TECHNOLOGY ASSESSMENT, AND ANALYTICS,

                U.S. GOVERNMENT ACCOUNTABILITY OFFICE,
                          HUNTSVILLE, ALABAMA

        Dr. Howard. Chair Booker, Ranking Member Cotton, and
    Members of the Subcommittee, I am pleased to be here today to
    discuss technologies that can assist in criminal
    investigations.
        Criminal justice is an important governmental
    responsibility with a significant impact on the public. Such
    investigations should be conducted as quickly and accurately as
    possible. A number of algorithms can aid investigators in the
    search for links between individuals and evidence collected at
    a crime. Some of these tools are enabled by artificial
    intelligence, while others rely on statistical or automated
    methods rather than AI.
        My testimony today is based on two reports we issued,
    describing the strengths and limitations of the most common
    algorithms used for criminal investigations. In our work, we
    found that Federal law enforcement agencies primarily use three
    types of algorithms in their investigations; probabilistic
    genotyping, latent print analysis, and facial recognition.
        These algorithms offer some common advantages over human
    analysts. For example, they dramatically increase the speed of
    comparisons, allowing larger data bases to be searched in
    significantly less time. In addition, they generally produce
    more consistent results because they don't get tired or have an
    off day as human analysts can. The best of these tools are
    highly accurate and effective, often more so than a human
    analyst alone.
        However, this is not to say that such algorithms are
    perfectly accurate or objective. There are hundreds of
    algorithms in development and on the market, and performance
    can vary significantly among them as shown in independent
    testing by the National Institute of Standards and Technology,
    NIST. Studies have shown that the best results may be obtained
    from the combination of a highly accurate algorithm and a well-
    trained analyst.
        Despite the advantages offered by high quality algorithms,
    there are also several challenges to their use. For example,
    poor quality crime scene evidence can significantly increase
    the effectiveness of the tools, and experts told us there is
    considerable variability in the standards for what constitutes
    suitable evidence quality, especially across the many State and
    local law enforcement agencies.
        Other challenges include errors or improper use by human
    analysts, unknown and potentially biased training data for AI-
    based algorithms. The possibility that the correct person isn't
    in the data base used for comparison. Difficulties with
    interpreting and explaining the output. Lack of the necessary
    information for law enforcement agencies to identify the best
    performing algorithms, and sometimes a lack of resources to
    purchase those. And a lack of public trust in the results for
    some algorithms.
        We proposed three policy options that may help address
    these challenges. First, increased training for analysts and
    investigators, which could improve their understanding of the
    importance of crime scene evidence quality. Increased training
    could also reduce errors and inconsistency by human analysts
    and increase proper interpretation of algorithm results.
        Second, policymakers could facilitate the development and
    adoption of standards for appropriate use, which could set
    standards for algorithm performance that is acceptable, reduce
    the use of low-quality evidence, and increase public confidence
    in the results.
        And third, increase transparency regarding the testing,
    performance, and use of algorithms, which could make it easier
    for law enforcement entities to identify the best performing
    technologies, and improve understanding of the training data
    for AI-enabled tools, perhaps leading to the development of
    more robust and representative training data.
        Finally, I note that there are many other types of
    algorithms in development or use beyond the three we examined.
    Some of these depend on AI to power their comparisons and
    decisionmaking. In our judgment, the challenges we identified
    are likely to also be relevant to these other types of
    algorithms.
        In conclusion, we found that probabilistic genotyping,
    latent print analysis, and facial recognition can be very
    useful to help investigate crimes, but several challenges may
    limit their effectiveness. By addressing these challenges, we
    can improve the accuracy and effectiveness of criminal
    investigations and enhance public trust.
        This concludes my prepared statement, and I would be happy
    to respond to any questions.
        [The prepared statement of Dr. Howard appears as a
    submission for the record.]
        Chair Booker. Dr. Howard, I'm really grateful. Professor
    Wexler.

                    STATEMENT OF MS. REBECCA WEXLER,

                      ASSISTANT PROFESSOR OF LAW,

                      UC BERKELEY SCHOOL OF LAW,
                          BERKELEY, CALIFORNIA

        Professor Wexler. Mr. Chairman, and Members of the
    Subcommittee, my name is Rebecca Wexler, and I am a co-director
    of the Berkeley Center for Law and Technology, and an assistant
    professor of law at Berkeley Law School. I'm honored to testify
    here today about the need for fair and open proceedings to
    scrutinize AI tools used in the criminal legal system.
        Although AI tools present exciting opportunities to render
    the legal system more accurate and equitable in some respects,
    they also present troubling obstacles to fair and open
    proceedings. To help fix this problem, Congress should do two
    things. First, require that AI tools used in the criminal legal
    system be made available for auditing by independent
    researchers with no stake in the outcome. Second, prohibit
    either party in a criminal case from invoking the so-called
    trade secret privilege to block access to relevant evidence,
    and instead require that such evidence be disclosed under a
    reasonable protective order.
        Zooming out, the U.S. criminal legal system is a model to
    the world in its commitment to fair and open proceedings to
    protect against wrongful convictions. That reputation rests on
    the safeguards we offer those accused of crime, including a
    fair opportunity to uncover weaknesses in the government's
    evidence of guilt, and to expose unlawful and unconstitutional
    police conduct.
        This is why the Sixth Amendment guarantees the accused the
    right to confront witnesses against them, and to compel
    witnesses in their favor. It's why due process and statutory
    discovery laws require prosecutors to disclose evidence to the
    defense. It's why the Supreme Court has directed judges
    deciding whether to admit scientific and technical evidence, to
    consider whether the evidence has been subject to peer review.
    Fair and open proceedings in which evidence is subject to
    robust and independent adversarial scrutiny are necessary for
    accurate, accountable, and legitimate criminal investigations
    and prosecutions.
        These transparency commitments are all the more important
    in the emerging age of AI because we are together facing a new
    form of evidence that by its nature, cannot be scrutinized with
    the tools that parties have traditionally used, such as cross-
    examination and classic detective work. In turn, allowing AI to
    enter criminal courtrooms without sufficient scrutiny is
    dangerous.
        While AI has the potential to improve law enforcement
    efficacy, it can also cause harmful errors with high-stakes
    consequences for life and liberty. For instance, erroneous AI
    face recognition hits and gunshot alerts have led to multiple
    alleged wrongful arrests and imprisonment. A recent study found
    that state-of-the-art AI photo forensic program, performed
    worse than regular people with no special training, and two DNA
    analysis forensic software programs came to divergent results
    about whether a defendant's DNA was included in a crime scene
    sample in a homicide case.
        We need fair and open proceedings to expose these kinds of
    costly mistakes and discrepancies. Unfortunately, some vendors
    block independent review that might expose flaws in their AI
    products. For instance, consider probabilistic genotyping
    software, the GAO Office, the PCAST report, and NIST all
    expressed concern about the lack of independent review of these
    tools. Yet, when fellow academic researchers and I sought to
    purchase a research license to study one of them, the vendor, a
    company called Cybergenetics, told us, ``Cybergenetics does not
    provide research licenses.''
        Representatives of this company have testified under oath
    in courts across the country that their product is subject to a
    peer review process and hence its output should be admissible
    in criminal cases. But when we actually tried to perform
    independent research into quality assurance and validation, the
    company stopped us from doing it.
        Congress should require vendors to allow independent audits
    by clarifying that AI tools used in the criminal legal system
    must be subject to independent review. There's an important
    role for Congress here to authorize Federal grants for law
    enforcement agencies to purchase AI systems only if the vendors
    make them available for auditing by research groups with no
    stake in the outcome.
        In another obstacle to fair and open proceedings, some
    vendors have also relied on a so-called trade secret privilege,
    to refuse to disclose details about how their technologies work
    to criminal defendants and expert witnesses, even under a
    protective order, and even in capital cases where the risk of
    error is wrongful death.
        This should not be happening. We need criminal defense
    counsel and experts, as well as judges and prosecutors, to
    scrutinize AI systems to ensure accuracy and fairness. There's
    no good reason for trade secret law to block this crucial
    process. Once again, there's an important role for Congress to
    clarify that no trade secret privilege exists in federal
    criminal cases, and relevant evidence must be disclosed under a
    protective order.
        Thank you for the opportunity to testify here today.
        [The prepared statement of Professor Wexler appears as a
    submission for the record.]
        Chair Booker. Thank you for the strong testimony. I'm here
    for the duration, so I'm going to go to Senator Butler. Then
    we're going to come back and go to the Ranking Member, Cotton.
    Then we're going to go to Whitehouse, and unless some nobody
    else comes, I will followup in the end. But we'll go to Senator
    Butler representing the great State of California.
        Senator Butler. Thank you, Senator Butler--you can be
    Senator Butler, too.
        Chair Booker. Hey, Butler's a kick up.
        Senator Butler. Senator Booker, thank you so much, and to
    our Ranking Member for hosting such an important conversation.
    I will sort of skip the preface and really jump right into what
    I think is a very important topic to examine. Dr. Howard, if
    it's okay, I'll start with you, and I'll try to land in my
    constituents' basket there.
        But Dr. Howard, you mentioned in the three recommendations
    that you would invite Congress to act in this space of
    increased transparency. I'd like to hear you talk a little bit
    about what kinds of transparency you think are needed. I'm
    going to direct this question to Professor Wexler as well, but
    I'd love to start with you and just sort of hear your thoughts
    there.
        Dr. Howard. Certainly, and we, in making our policy options
    for policymakers, we try not to be too prescriptive----
        Senator Butler. Sure.
        Dr. Howard [continuing]. We want to leave room for the
    policymakers, but some of the things we heard about from
    experts we spoke with include being transparent about the
    training data bases. For example; how representative are they?
    Where are they drawn from? Are they representative of the kind
    of evidence that might be collected at a crime scene? The sort
    of print quality that might be expected, or photo quality that
    might be expected.
        Also, increased transparency about whether they've been
    tested, and how, and what were the results of those tests so
    that everybody understands how does the algorithm perform under
    ideal conditions, under less-than-ideal conditions, which are
    often present in a criminal investigation? And can then make a
    determination about how much weight to give the evidence that
    is produced by the algorithm?
        Senator Butler. And then, Professor Wexler, what would you
    add to it? I know there are a couple of specific areas that you
    also recommend increased transparency. You ended your testimony
    in the space of auditing. What other recommendations would you
    add there?
        Professor Wexler. Thank you for the question. I would add
    that the right test is the baseline relevance test. So by
    default, criminal defense counsel is entitled to discover
    relevant evidence on a case-by-case basis. That's the threshold
    that they should have to show in order to get access to any
    details about evidence used against their client.
        What's the problem is when that threshold burden is raised
    to a necessity burden, for instance, based on some so-called
    secrecy interest. And the secrecy interest, if it's
    intellectual property, is not a legitimate one to do that.
        Senator Butler. So stay, stay there for just 1 second
    because I did want to talk just a little bit about public
    defenders. And we all know, have seen the data, particularly in
    metropolitan communities around the country, the overworked
    nature and caseload management of public defenders. You talk a
    lot about the potential challenges, the need for transparency.
    Share just a little bit about how the failure to disclose the
    usage of AI might enter the consideration or the management of
    the case workload for public defenders.
        Professor Wexler. Sure. So I have two thoughts on this. One
    is that because AI systems are used across many different
    cases, even if you have a public defender, or even private
    defense counsel who's particularly well-resourced, so say in a
    centralized office that has enough counsel to have specialized
    counsel focusing on DNA or other forensic technologies, even if
    disclosure goes just to--if it goes to everybody, but those
    well-resourced counsel are able to identify flaws or weaknesses
    in the technology, those identifications benefit everyone as a
    whole.
        So even though not every counsel will have the bandwidth to
    address it, disclosure is still beneficial. And I can give a
    concrete example of where disclosure was beneficial in one
    case, if that would be helpful. The Office of the Chief Medical
    Examiner of New York City created a forensic software program
    called FST to analyze DNA evidence. And for years, they refused
    to disclose source code for that tool, claiming they had a
    trade secret interest in withholding it.
        In one case, they finally were ordered to disclose by Judge
    Valerie Caponi, former general counsel of the FBI in the
    Southern District of New York. And the defense expert witness
    who reviewed it in that case uncovered a undisclosed function
    that discarded data in certain circumstances, and had been
    added after the New York State Forensic Science Commission's
    regulatory approval for the tool. So that discovery happened in
    an individual case and was beneficial, was useful information
    for many other defense counsel as well.
        Senator Butler. Thank you so much, and thank you, Mr.
    Chair.
        Chair Booker. Thank you very much, Senator. Ranking Member
    Tom Cotton.
        Senator Cotton. Mr. Aguilar, you mentioned in your opening
    testimony that tip lines are like Crime Stoppers. You had also
    said last year in the interview that your department treats
    facial recognition technology, ``like a tip that is called into
    Crime Stoppers.'' Is that correct? Do you recall that?
        Mr. Aguilar. Yes, Senator.
        Senator Cotton. So in your career as a police officer, have
    you ever gotten erroneous tips from Crime Stopper hotlines?
        Mr. Aguilar. At least tips that we couldn't corroborate
    with other evidence. Absolutely, Senator.
        Senator Cotton. Okay. Have you ever considered eliminating
    Crime Stopper hotlines because you get erroneous tips?
        Mr. Aguilar. Absolutely not.
        Senator Cotton. Okay. You're not testifying that, say,
    facial recognition technology or ballistic technology is
    flawless. Are you?
        Mr. Aguilar. I'm not.
        Senator Cotton. Okay. Let's say that you have facial
    recognition technology, and you run a security camera image
    through that technology, and it gives you back seven potential
    positives. What's the next steps do you take? Do you go out and
    arrest all seven individuals?
        Mr. Aguilar. Absolutely not, Senator. We would treat the
    matches as if we had just received seven Crime Stoppers tips,
    and our detectives would do their due diligence, and either try
    to discount or corroborate it with other evidence that could
    tie that person to the crime scene
        Senator Cotton. Using other technology or even artificial
    intelligence technology or using traditional gumshoe
    investigative techniques?
        Mr. Aguilar. All of the above, Senator. This is absolutely
    not a substitute for traditional investigative methods. It
    compliments traditional investigative methods, but in no way
    can AI, at least in my view, substitute traditional methods.
        Senator Cotton. Okay. What would be the consequence if you
    did not have this kind of technology in your department?
        Mr. Aguilar. I'm very certain that we would have many more
    crimes that would go not only unsolved, but there's an
    additional consequence to having unsolved crimes, and that is,
    that those criminal suspects are allowed to continue to
    victimize other people. And so it's not just a lower clearance
    rate, but it's also a higher victimization rate.
        Senator Cotton. Okay. Ms. Wexler, one of your chief points
    seems to be that the vendors who provide such technology should
    be compelled to disclose their source code. Is that correct?
        Professor Wexler. If the source code is relevant evidence
    in a particular case, then that should be the baseline
    standard, and should be disclosed under a protective order that
    protects the intellectual property interest to a reasonable
    degree.
        Senator Cotton. Could you give examples, again, of when
    that would be relevant evidence? Because as Mr. Aguilar said,
    if you get seven positives from facial recognition technology,
    that itself is not going to be what cinches is the case, or
    gets a conviction, or perhaps even admitted into court. It's
    going to be the follow-on investigative techniques that his
    detectives use.
        Professor Wexler. Sure. So relevance means any evidence
    that makes the fact at issue in a case more or less likely than
    it would be without the evidence. So it's actually a pretty low
    bar. I tell my students a brick is not a wall. So evidence
    doesn't have to disprove the whole case in order to be
    relevant. It just needs to weaken or strengthen one element of
    it. So any information about how a forensic technology works
    that could expose unreliability of a particular match would be
    relevant.
        Senator Cotton. Let's use something a little more high tech
    than, say, facial recognition, which we all engage in every
    single day, let's say ballistic matching. If you have AI-
    assisted ballistic matching, is that the source code of that
    technology that's what's relevant, or is it going to be the
    human testimony that follows on that, presumably, from dueling
    witnesses the State uses and that the defense uses? Does the
    source code matter once you have those dueling witnesses in the
    follow-on question?
        Professor Wexler. So various aspects of the technology
    might be relevant in a particular case. A source code might not
    always be relevant, but it could be in a particular case. Even
    if an AI technology is then being reviewed, the conclusions are
    reviewed by a human witness, the AI results may have bias to
    the human witness.
        So it'd be important to understand if they were flawed. AI
    results may, in face recognition technology, produce alternate
    candidate lists that could be exculpatory evidence that would
    need to be disclosed to the defense. So those are examples.
        Senator Cotton. Why do you think these vendors don't want
    to disclose your source code to you and your fellow law
    professors or to criminal defense attorneys?
        Professor Wexler. So to be clear, my fellow academic
    researchers----
        Senator Cotton. And you-all wanted a license. Why do they
    not want to give you a license to use it, and why do they not
    want to disclose it to criminal defense attorneys?
        Professor Wexler. In my personal view, and of course I
    don't have insight into their own heads, is that they are
    concerned about subjecting their products to robust adversarial
    scrutiny because that scrutiny might expose weaknesses or flaws
    in their tools.
        Senator Cotton. And these products have undergone testing
    by, say, our own Department of Justice. Haven't they?
        Professor Wexler. Some of the products have undergone
    testing and some of them less so. So in terms of probabilistic
    genotyping software programs, again, the GAO Office, NIST, the
    PCAST report have all identified concern that much of the
    testing was not performed by independent researchers. And most
    recently, NIST identified that some of the published validation
    studies don't have enough publicly available data to be
    independently verified.
        Senator Cotton. Mr. Aguilar, do you have any thoughts on
    why these vendors might not want to disclose their source code
    to criminal defense attorneys, or provide research licenses to
    professors?
        Mr. Aguilar. Senator, I cannot speak for the vendors. I can
    only imagine that there's at least some concern over
    intellectual property, but I certainly cannot speak for the
    vendors.
        Senator Cotton. Okay. And Professor Wexler, you keep saying
    protective order. There, you're talking about disclosure of
    source code in a criminal proceeding. Right?
        Professor Wexler. It could be disclosure of any aspect of
    the technology. So vendors, these companies, have actually
    alleged that trade secret privileges should apply to all sorts
    of aspects of these tools, including things like user manuals.
    So, it's not limited to source code.
        But I will say that, you know, courts order disclosure of
    intellectual property under protective orders, reasonable
    protective orders all the time. It's a common solution in civil
    cases, and if it's effective there, it should also be deemed
    sufficient in criminal cases where the stakes are life and
    liberty.
        And I'll add that protective orders give intellectual
    property holders more safeguards than the non-disclosure orders
    that companies usually--non-disclosure agreements, I'm sorry,
    that companies usually disclose the same information under for
    when they share it with their employees or with business
    negotiators.
        So the protective order, if there were to be a leak, rare
    instance, there's not evidence that protective orders are
    generally violated or commonly violated. But in the rare
    circumstance where that might, happen trade secret holder would
    still be entitled to sue for misappropriation of their
    intellectual property.
        And on top of that substantive trade secret guarantee,
    there would be the additional guarantee of criminal or civil
    contempt of court plus the possibility of disciplinary
    sanctions if it were an attorney who violated the protective
    order. So given those safeguards, trade secret law does not
    provide a good reason to withhold relevant evidence in a
    criminal case.
        Senator Cotton. Mr. Aguilar, in your long history of
    criminal proceedings, has a protective order ever been
    violated?
        Mr. Aguilar. No instances come to mind, Senator.
        Senator Cotton. Okay. Thank you.
        Chair Booker. Thank you for that really thorough
    questioning. Senator Whitehouse.
        Senator Whitehouse. Could I ask each of you to consider in
    the law enforcement arena what you believe to be the most
    promising use of AI that we in Congress should consider
    encouraging, and what you consider to be the most dangerous use
    of AI that we should be more closely monitoring? Just go right
    across, if you don't mind, Aguilar, to Howard, to Wexler.
        Mr. Aguilar. Senator, it's very difficult to list any one
    that has the most promise, and I can cite several examples
    where we've successfully solved everything from violent crimes
    to property crimes using a number of AI tools. I think that the
    greatest threat comes in the absence of sound policy, and that
    applies to any tool that law enforcement uses.
        So again, sometimes we may think of AI as something new and
    a challenge that we've never faced. But the reality is that law
    enforcement has, for many years, had access to sensitive
    information through driver's license data bases, criminal
    records, and it's the policy around those systems that really
    safeguards the public.
        The same can be said for other tools that we use; our
    weapons, our vehicles. We don't rely on manufacturers to write
    our deadly force policies or our driving policies. So I think
    that the absence of policy would pose the greatest threat, and
    Congress should be looking at a number of AI tools that are
    right now making our community safer.
        Senator Whitehouse. Dr. Howard.
        Dr. Howard. The only AI-enabled tool that we examined in
    any detail was facial recognition, but I would say broadly, the
    most promising AI tools are those that are trained on a
    reliable and representative data set that is well understood,
    and that is----
        Senator Whitehouse. Like ballistic information?
        Dr. Howard. We have not studied that one. So I can't affirm
    that, I'm sorry.
        Senator Whitehouse. There's plenty of it though.
        Dr. Howard. And that also I have a well-trained analyst and
    investigator using them, because remember, every AI tool is the
    middle piece of an evidence process. Somebody collects crime
    scene evidence, prepares it for use, determines whether it's a
    suitable quality to be inserted into the algorithm, and then
    assesses the output that comes out of the other end and decides
    what to do about it. So that well-trained analyst is key.
        The riskiest AI tool would be one where the training data
    set is not understood, not representative, and it's being
    handled by somebody who really doesn't understand what the
    technology is and isn't telling them in the output.
        Senator Whitehouse. Thank you. Professor Wexler.
        Professor Wexler. I think that it would be promising for
    Congress to support AI that is aimed at uncovering exculpatory
    evidence. One of the concerns that I have about the markets for
    AI tools is that most of the paying customers are law
    enforcement, and defense experts and defense investigators
    don't have the same money to throw at the problem. And this may
    bias the development of technologies in favor of identifying
    evidence of guilt rather than identifying evidence of
    innocence.
        So any support Congress could give to AI technologies
    designed to identify evidence of innocence would be very
    promising, and my biggest concern are technologies that are
    kept secret and withheld from adversarial review by the
    vendors.
        Senator Whitehouse. And Mr. Aguilar, you might have
    mentioned this, but with your experience on law enforcement,
    have you had occasion to use any kind of AI technologies with
    ballistics information, with cartridges, spent bullets?
        Mr. Aguilar. Absolutely, Senator. So with our partnership
    with ATF, we do have an in-house ballistics program, a crime
    and gun intelligence center. And it's one of many tools that we
    employ to investigate and prevent violent crimes. It's
    certainly been helpful.
        Senator Whitehouse. Is there an AI component though?
    Because before there was AI, there were still computer programs
    that compared, for instance, the markings on an ejected
    cartridge or the markings on a spent bullet. Two other markings
    in the same way that fingerprint searches take place. Not so
    much with modern AI, it's just computer data bases.
        Mr. Aguilar. So our definition of AI sometimes changes.
    Right? As technology becomes more mainstream, we sometimes
    think of it as less AI than the newer technologies. But if we
    look at ballistic evidence where it's compared through an
    automated system in a data base, and then later it has to be
    verified by a human analyst, and it makes connections through
    the automated process that has to later be verified by a human
    analyst. So in that sense, I certainly consider it AI as it has
    to be human-verified.
        Senator Whitehouse. Thanks very much, Chairman.
        Chair Booker. Thank you very much. I'm not sure who was
    here first. Padilla. California is well represented today.
    Senator Padilla.
        Senator Padilla. Thank you, Mr. Chair. Speaking of
    California, it was recently reported that a local police
    department in my State used AI to render a 3D image of a
    suspect from crime scene data DNA, and then attempted to run
    that image through facial recognition software in an effort to
    identify a suspect.
        Now, this is the first reported case of law enforcement
    attempting to use facial recognition technology, which has
    questions in and of itself, on an AI-generated image. The
    incident first occurred, actually, in 2020. So it's been a
    couple of years, but the public did not become aware of it
    until it was reported this week. There appears to be a little
    transparency questions and concerns about how AI tools are
    being used by police and prosecutors during criminal
    investigations. And I cited just one example.
        First question is for Dr. Howard. What steps should be
    taken to ensure that defendants and the public are aware of
    when AI is being used in criminal investigations?
        Dr. Howard. Part of our policy option calling for
    transparency would envision a scenario where it is revealed
    when algorithms have been used in one form or another. And to
    speak to Mr. Aguilar's point, AI is often what we now think of
    as automated technologies, was originally considered AI, what
    most people call AI now is machine learning, which is trained
    on training data. We believe that transparency step should be
    known. People should be aware when an algorithm has been used
    in one form or another as part of the evidence collection and
    assessment process.
        We do believe, however, it's very important to validate the
    results of those methods. And in this case, the articles that
    I've read have not indicated whether any validity testing has
    been conducted on the reconstruction process for the face, and
    then whether those faces can be reliably run through even the
    best algorithms. We've not seen any data indicating that those
    have been tested.
        Senator Padilla. Exactly. So it brings up a whole host of
    questions. Followup question for Professor Wexler. Welcome from
    California. How critical is transparency for fostering a
    positive relationship between citizens and law enforcement for
    ensuring just results in criminal proceedings?
        Professor Wexler. Transparency is crucial for a positive
    relationship between citizens, law enforcement, and just
    results in criminal proceedings. Transparency is one of the key
    functions of the adversary system. We have an adversary system
    to enable the criminally accused to scrutinize and contest the
    evidence against them so that we can ensure that law
    enforcement's use of that evidence is effective, accountable,
    and legitimate.
        Senator Padilla. Now, question on a separate topic. Last
    September, the GAO released a report showing that several
    Federal law enforcement agencies, including the FBI and DEA
    among others, did not require their agents to be trained on
    protecting civil liberties and rights when using facial
    recognition software.
        As I mentioned earlier, facial recognition software begs a
    whole host of questions in and of itself. But this lack of
    training is troubling, given that the technology has been shown
    to produce biased results, particularly when it involves
    identifying black and brown persons. Back to Dr. Howard, since
    the release of this publication, have steps been taken to
    ensure that Federal agents are trained on how to responsibly
    use facial recognition technology?
        Dr. Howard. We are not aware of any changes as a result of
    that report. That doesn't mean they haven't occurred. We just
    have not been able to followup with the agency to see whether--
    --
        Senator Padilla. So then the bottom-line----
        Dr. Howard [continuing]. They've been trained.
        Senator Padilla [continuing]. Question is; what
    recommendations do you have to this committee, to the Senate to
    Congress, to advance this?
        Dr. Howard. One important feature of facial recognition is
    the need to test and verify the accuracy and the demographic
    bias. And this is one of the things that NIST does in its
    testing, but NIST does not exhaustively test every algorithm.
    It tests the algorithms that are submitted to it for testing.
        So, one possibility would be to require algorithms to be
    tested through an independent third party, such as NIST, if
    they're going to be used for Federal law enforcement purposes,
    that would add that measure of third-party independent
    accountability. If you're using the best tools, can an error
    still occur? Of course. Human beings make lots of errors as
    well, and many of the most highly publicized errors with
    forensic algorithm tools have actually been traced back to
    errors the human analyst made, for instance, submitting
    evidence that was not of high-enough quality to the algorithm,
    and the algorithms then unable to give a reliable result.
        Senator Padilla. Right. And you have confidence that NIST
    or any, whichever Federal entity ends up with this
    responsibility, has a capacity for determining guidelines for
    testing and certification of algorithms?
        Dr. Howard. We've not looked at that directly, but we do
    know that NIST is often tasked with the job of setting
    standards for such things and would seem to have the----
        Senator Padilla. That's the flip side. Right? You can do
    test all you want, but what are you testing for in determining
    what passes and what doesn't. Okay. Thank you. Thank you, Mr.
    Chair.
        Chair Booker. I think when you get two parts, California,
    one part Georgia, that is New Jersey, but.
        [Laughter.]
        Chair Booker. So with that, Senator, you have to bake at
    350 for about 45 minutes. But Senator Ossoff, thank you very
    much. Please proceed.
        Senator Ossoff. Thank you for convening us, Mr. Chairman,
    and thank you to our witnesses for your expertise.
        Beginning with you, please, Professor Wexler, what
    technologies or practices are being developed, or might need to
    be developed to make criminal investigations, law enforcement
    activity, civil litigation resilient against synthetic,
    fabricated evidence?
        There's much discussion about voice cloning and the rapid
    advance in that technology. You can imagine that being provided
    by a malicious third party to law enforcement to prompt a
    criminal investigation of a targeted individual, or other cases
    where such technology could be used to frame or improperly
    implicate somebody. How do you protect against that risk, and
    do you think it's right to think of that as a risk?
        Professor Wexler. Oh, I think it's absolutely a legitimate
    concern. And it raises the question of whether our current
    legal system and current evidence rules are up to the task of
    maintaining accuracy and adversarial review with new
    technologies like deepfake technologies available.
        So when AI-produced evidence or AI-analyzed evidence comes
    into court, risks of error are present just as they are with
    human witnesses. And yet, one of the new challenges is that
    some of the traditional safeguards we have for cross-examining
    human testimony don't apply to machine-generated outputs. So
    it's very difficult to put a AI output on the stand under oath
    and cross-examine them, and observe their demeanor. That's not
    going to work.
        So the technology, I would say, as a lawyer, that we need
    to develop to ensure proper safeguards is the legal technology
    of ensuring we have robust discovery and transparency rules
    available, perhaps more pretrial testing, access to these
    technologies for validation and scrutiny rather than simply
    relying on the current evidence rules as they exist.
        Senator Ossoff. Where AI is used for purposes of
    investigative analysis, transparency and discovery through the
    adversarial process could be sufficient. But what about
    verification? The verification of the veracity or authenticity
    of evidence that's presented in court?
        Professor Wexler. So for evidence presented in court----
        Senator Ossoff. Or let me say, or if it's presented as a
    predicate to open a criminal investigation prior to even
    arriving or perhaps never arriving in court?
        Professor Wexler. Excellent. So, right, as an evidence
    professor, I always have to tell my students, the evidence
    rules don't even apply if you never get to court. So what are
    you going to do? And there I would say, we really need
    independent researchers with no stake in the outcome to be able
    to have access to these technologies to test, and scrutinize,
    and validate them.
        And so one of the things that Congress could do here is
    require or incentivize through the Federal grants program for
    law enforcement assistance that AI technologies used in the
    criminal legal system are open to peer review or scrutiny by
    independent researchers with no stake in the outcome. And that
    will help with that verification piece, even before we get to
    trial.
        Senator Ossoff. And thinking about the rules of evidence
    and discovery, if synthetic and inauthentic evidence were used
    to open a criminal investigation, and the subsequent progress
    of the investigation yielded for criminal investigator
    sufficient evidence to prosecute, and the initial underlying
    inauthentic and synthetic evidence that was used to justify the
    progress of the investigation is never presented in court.
    Would a criminal defendant ever have means of knowing that at
    the very beginning of the process that led them to this
    position was something artificial or synthetic?
        Professor Wexler. Under current law, I don't believe there
    would be a requirement to disclose. There's not a clear path
    for that, and that's a concern. There's a process called
    parallel construction where law enforcement may undergo
    multiple investigations, and present one path toward finding
    evidence in court and not disclose the other paths. So there
    may be an opportunity and a role here for Congress to mandate
    affirmative disclosures of investigative strategies.
        Senator Ossoff. And that's a concern as well, isn't it,
    where foreign intelligence information may be the root of a
    criminal investigation, but through parallel construction,
    sufficient evidence is developed by investigators and
    prosecutors such that the origins of the investigation being
    rooted in foreign intelligence is never disclosed to the court
    or to the defendant.
        Professor Wexler. There certainly are concerns about that.
    Yes.
        Senator Ossoff. Dr. Howard, any thoughts on verification of
    evidence? Think about deepfakes voice cloning.
        Dr. Howard. So we've done limited work on deepfakes. It was
    not a part of the work I'm testifying about today, but the fact
    that the current state technology is able to detect deepfakes,
    it sometimes takes time. It's not necessarily a quick process.
    And with deepfakes, that can be an issue if something is out in
    the public and causing harm, and we can't identify it quickly
    enough.
        In the cases you're talking about, criminal investigations
    can take a long time, prosecution can add time to that as well.
    So one would hope the technologies would allow us to identify
    synthetic evidence before it were brought to court, for
    example, in the case that Professor Wexler might be
    hypothetically talking about.
        But the tools in any kind of a fraud-type scenario, the
    tools are almost always a step behind the fraudsters. They're
    creating some new way to do something, and those who are trying
    to detect it are just a step behind them.
        Senator Ossoff. One more question.
        Chair Booker. Of course. Go ahead.
        Senator Ossoff. So, or as Professor Wexler noted, the
    underlying synthetic evidence may never be brought into court,
    may never be scrutinized, may never be subject to verification.
    Dr. Howard, is your impression that the technology--have you
    done any study, do you have a sense of whether the technology
    for verification, for detection of synthetic evidence such as
    voice cloning is expected to stay ahead of the capacity for
    synthesis and production of deepfakes, or are we expecting
    instead that the apparent authenticity of deepfakes will become
    so sophisticated, so advanced that it defies verification?
        Dr. Howard. We don't have any direct evidence either way,
    but historically speaking, the detection tools are normally
    just a step behind the new creations of ways to perpetuate
    fraud. One might assume the same would occur here, but we have
    no evidence of that in either direction. We don't see any
    evidence at the current time of something being created that's
    totally undetectable. It just may take time.
        Senator Ossoff. Thank you. Thank you, Dr. Howard. Thank
    you-all.
        Chair Booker. Thank you very much. Professor Wexler, I
    really do question whether Senator Cotton has seen WALL-E. But
    otherwise, I actually think he's a really intellectually
    rigorous--and I don't always agree with his conclusions, but
    he's an intellectually honest person, and I think where he was
    going is really substantive, which is this idea of somehow the
    AI technology companies that we're talking about, that you
    rightfully asked for, you know, adversarial scrutiny, peer
    reviews, and how urgent it is for us to get over this trade
    secret privilege.
        I guess the concern that, I think, that he was getting at
    was that those companies do have a legitimate--and again, might
    be cynical to say that they're just interested in being able to
    continue to sell their product. But you had some good retort
    for the idea, and I want to scratch it a little bit more, that
    those companies get more protection by opening themselves up to
    that scrutiny. Could you just explain that to me a little bit
    more?
        Professor Wexler. Sure. Absolutely. So substantive trade,
    secret law is a body of intellectual property that creates a
    negotiation between the public interest in accessing
    information and the goal of incentivizing innovation by
    providing property rights to the owner. And so there are
    limitations all the time to substantive property rights. Patent
    law has an expiration date, and trade secret law has some
    limitations as well that are part of that policy balance. The
    limitation of trade secret law is that you only get a right
    against misappropriators, which is wrongful acquisition, use,
    or disclosure.
        And my position is that cross-examination in court is not
    misappropriation. So the only time the intellectual property
    interest would kick in is if somehow a protective order, if
    information's properly disclosed under the protective order
    subject to cross-examination for purposes of a criminal case,
    that limited case, could be attorney's eyes only, expertise
    only. The only reason that a company might have a legitimate
    concern is if that protective order might leak.
        And what I was trying to explain, and I'm happy to have
    another opportunity to elaborate, thank you, is that in that
    unlikely event, and there's not evidence that this happens
    regularly, in the unlikely event that a trade secret were to
    leak when it was subject to a protective order, all of the
    substantive trade secret protections are still available to the
    intellectual property owner. They can sue for misappropriation,
    and yet, there's even more safeguards than normally occur
    outside of court because you have criminal contempt of court
    charges.
        Chair Booker. Okay. So I understand that, and that's
    really, I think, a substantive point. And so to the extent you
    can, the only jeopardy there is you don't see any real business
    pecuniary jeopardy to that person exposing their algorithms or
    their processes to peer review. You really can't think of any
    other business threat to them because, clearly, they're getting
    some additional safeguards. Is that what you're saying?
        Professor Wexler. Correct. There's no legitimate business
    concern with disclosing the information for cross-examination
    under protective order for the limited purpose of ensuring
    accuracy in a criminal case.
        Chair Booker. And then what about a prosecutorial concern?
    In all sincerity, could there be a concern amongst the
    prosecutors?
        Professor Wexler. I believe that the trade secret privilege
    should not be available to either party to claim in a criminal
    case, so that this would be a parallel and equal requirement of
    disclosure.
        Chair Booker. Okay. Let me take off my Senator Cotton hat.
    He does a much better job at being Senator Cotton than I do.
    Although I can ask the staff, maybe, am I stepping up to the
    plate?
        And now let me ask one of my concerns, which is 98 percent
    of criminal convictions is done during plea bargaining and has
    nothing to do with trial. And I'm wondering about this element
    that you said where--and Senator Cotton did a good job, I
    think, also of bringing out this fact that as Chief Aguilar
    said that, well, the AI gets me to a point, but my analysts,
    and my experts, and my good gumshoe police work--I need to know
    where that gumshoe, like, where does that come from? Is there
    really gums on their shoes. Explain this to me someday, please.
    But that it really is not--I'm not relying solely on the
    technology. This has now been reviewed by expert human
    witnesses. So that renders your desire to break open my
    algorithm and all of that moot, because now it only led me to
    knowing it's one of seven people. Why should I be subject to
    that kind of scrutiny now?
        Professor Wexler. Great. So I have two responses. The first
    is to say defendants or prosecutors are only entitled to
    discover relevant evidence. So if information truly is moot, in
    other words, there's no possible way that disclosing the
    information could render a fact at issue, more or less likely
    than it would be without it, it's not discoverable. It's
    already protected. You don't get it. So we're only talking
    about relevant information.
        Now, my second point is there may be all sorts of
    circumstances where it could be relevant to discover
    information about the algorithm, despite the fact that a human
    has reviewed it after the fact. And I certainly can't
    anticipate all those circumstances on a case-by-case basis, but
    I can think of a few.
        An example would be if the algorithm, say a face
    recognition system, were known to the analyst who's reviewing
    it, and potentially biased to that analyst to have
    overconfidence in their conclusion, you'd want to know then
    whether it was overconfidence, how good was that algorithm?
    Were they right to trust it so much?
        Another example might be that the algorithm could have
    produced alternate exculpatory information that wasn't
    disclosed by the human analyst because it didn't fit with the
    analyst's own conclusions. And that could help as well.
        Chair Booker. A brilliant point. And so the last thing I
    would ask you is you said you give your law students this,
    ``the brick is not the wall.'' Can you explain that to me
    again? Because I went to Yale Law School, as you know, that's
    an inferior law school compared to Boalt, but go ahead.
        Professor Wexler. Yes, thank you. You know, I don't even
    know where the phrase comes from, but a brick is not a wall
    means relevance is a relatively low bar. It doesn't mean
    nothing, but it's not an onerous threshold like showing that
    this piece of evidence is going to prove your whole case, no.
    It just means you get information, and you get to introduce it
    in court if it will make a fact, an issue ever so slightly more
    or less probable. And then with all those individual pieces of
    information, all those little bricks that are each relevant on
    their own, you build a case.
        Chair Booker. Right. And then finally, there's this big--
    there's a great book ``Why Innocent People Plead Guilty.''
    Because they face such an enormous stacking of criminal charges
    and the enormity of it all. And I guess we could imagine a
    situation where a prosecutor is sitting there saying, ``Take
    this deal. We'll give you 5 years, or else you're going to face
    50, because we've got this evidence based upon AI.'' That's a
    very intimidating moment for an overworked--as one of my
    colleagues was saying--defense attorney. Is that a concern of
    yours?
        Professor Wexler. Absolutely a concern. I absolutely agree.
    And I'll just add to this concern that many indigent defense
    counsel may not have the resources to challenge the AI, but
    also individual defendants might--the cost of fighting the
    case, even when they maintain their innocence, could be so
    great; the risk of losing your job, risk, of losing custody of
    your children, your home that it is perfectly rational to plead
    guilty even when you haven't committed the crime.
        Chair Booker. All right. Chief Aguilar, it's interesting as
    an African American living in an African American community
    that's highly impacted by crime, that you have these two
    dueling reactions. One is you see how unfair the criminal
    justice system is. You know, I grew up in a predominantly white
    area, went to Stanford, Yale, saw lots of drug use, the
    enforcement of which was nil to nothing. But in communities of
    color like mine, you see a tremendous amount of drug use.
        And then on the flip side, you see all these crimes, as you
    were eloquently putting, that are going on your neighborhood
    that aren't being solved. So you have this almost double down
    discontent with your public safety, and that's a problem.
    There's one great criminal justice writer that said that black
    communities have too much of the policing they don't want, and
    not enough of the policing that they need in terms of, as we
    said, the closeout rates of murders, and shootings, and the
    like.
        But your story's extraordinary. So I'm so excited that you
    came as a witness today because you are working so hard to
    restore that trust within communities and that technology, and
    I saw this where the ACLU was dead set against me using a
    camera technology and ShotSpotters.
        We invited them in to help us write the operating
    procedures, and then how much my community wanted this
    technology being used on their streets because, like, you don't
    live here. We want cameras on our streets and the like. So I
    guess this tension that I've lived my life is something you
    struggle with every day. And technology brings a whole new era
    of challenges to something that's so important for law
    enforcement, which is police community trust.
        And so I'm hoping with that context, could you sort of tell
    me what your hope is amidst this for restoring the kind of
    trust needed for communities to be safe? And then, what is the
    excitement that you have as you see the future of this
    technology to even better help the most impacted communities
    have the kind of safety and security every community deserves?
        Mr. Aguilar. So, Senator, to your point. Absolutely. You
    know, we know that, at least through my experience, in the
    communities that are the most affected by gun violence, I have
    never heard anyone in those communities say we need less of
    this. They want everything that we can throw at the problem so
    that their children stop dying. Right. That's the overriding
    concern in the communities that are most affected by gun
    violence.
        And so what I've found is that these tools have helped us
    hone in on those people that are the drivers of repeat gun
    violence. We know through numerous studies that it's usually
    about 5 percent of our gun offenders that are driving 50
    percent of the shootings. So AI has given us the opportunity,
    along with traditional policing methods, where we employ micro
    hotspot policing, where we focus on repeat offenders.
        We saw, for example, a disturbing trend in some domestic
    homicide cases where those incidents were preceded not by
    felony incidents of domestic violence, but by misdemeanors that
    perhaps we couldn't get to quick enough. And so we found a way
    to identify those people that were repeat offenders for
    domestic violence, that we're also carrying out other crimes to
    where when we're talking about carrying out enforcement that's
    preventative in nature, we're not targeting and entire
    communities so much as we're targeting those 5 percent of the
    offenders and 5 percent of locations that drive 50 percent of
    our violent crime.
        So I think that numerous studies have also shown that the
    more police officers we have out there, the more resources,
    human resources, the more positive our impact on crime rates.
    But it goes beyond that. Right. We have to just also embrace a
    lot of these technologies that help us focus on the right
    people that are driving violence in our communities.
        Chair Booker. And technology's clearly a force multiplier.
    It allows one individual to do a lot more work a lot more
    quickly, and you've been a homicide investigator so you
    understand that. Yes?
        Mr. Aguilar. These technologies can cut out hours, even
    weeks' worth, of time in certain instances where we're talking
    video analytics, where we can get several weeks' worth of
    footage and be able to compress it to those points that are
    most relevant and of evidentiary value absolutely.
        Chair Booker. And then, how do you see--is there anything
    on the frontiers that gets you really excited about where you
    think policing might be with these tools in 5 years, 10 years
    from now?
        Mr. Aguilar. I think that a lot of the technologies, just
    like I mentioned earlier with ballistic evidence to where we
    sometimes have a hard time considering that artificial
    intelligence, a lot of these technologies are going to become
    more mainstream. We use with facial recognition as an example.
    We use it in our daily lives just to get into our phone several
    times a day.
        And so, I think that these technologies hold a great deal
    of promise. I think that they become smarter and more accurate
    with time. We just have to put behind it the responsible policy
    and the right amount of research. The NIST facial recognition
    vendor testing, for example, is an excellent resource. If we
    were to consider--if Congress were to consider Federal funding
    for some of these technologies, you know, comparing it to
    sources that are available like the NIST vendor testing, it
    would be greatly helpful.
        Chair Booker. What about training and accountability of
    police officers? I mean, my police union resisted bodycams, and
    then it shifted really quickly. They appreciated them because
    for citizen complaints, it was a very good video to have. My
    former director now thinks bodycams is great for--all that
    footage is great for training because you can break down
    officer interactions.
        I'm imagining that could be done with AI looking at the
    volumes and hours and hours looking for certain patterns that
    might help to create more accountable policing. Have you used
    it all to hold your police to higher levels of accountability
    and transparency?
        Mr. Aguilar. So, Senator, to your first point there,
    there's never enough training. And so, I am always a fan of
    more and better training. Right now, we are looking into a
    research partnership to where we will use artificial
    intelligence to go through those hours and weeks' worth of
    bodycam footage to not only highlight problematic behavior, but
    also commendable behavior.
        Chair Booker. Yes. You know, I just had a sheriff in New
    Jersey commit suicide. I have seen officer mental health
    challenges. I was just talking to a reporter about the Obama's
    21st Century Task Force on Policing, and they saw a lot of
    predictive analytics for officer misconduct. One of them, which
    is actually doing suicide calls, seemed to be something that
    would pop up before an officer would have an interaction with a
    citizen that was negative. And I don't know if that's something
    you're thinking about how to see how AI might be able to help
    in that space to anticipate officer behavior who might need
    more training.
        Mr. Aguilar. Without a doubt, Senator. I think that any
    technology that'll help us flag problematic behavior--we've
    seen it. We've seen officers whose careers have come to a very
    ugly end, right, and they're often proceeded by problematic
    behavior where that officer could have benefited by from early
    intervention. And so, we as a police department can't correct
    problematic behavior that we're not aware of. And so, if this
    technology helps us get there, I'm all for it absolutely.
        Chair Booker. And so I would imagine--and I'm just on the
    last question then I'd like to move on to Dr. Howard. I do hear
    this though still in my community. My young kids are stopped
    and frisked more than any other community. This doesn't happen
    in wealthier, whiter communities. Now we're under video
    surveillance in ways that other communities are not. Now they
    have facial recognition that we're exposed to more than other
    communities. You can see how that list can go on, and on, and
    on, and on, which I wonder if people start to feel like they're
    under a surveillance state, or feel like they're having their
    just basic privacies. Could you empathize with that feeling, or
    is that just not your experience that you're seeing out there
    at all?
        Mr. Aguilar. So, Senator, I think that right now, video
    cameras are ubiquitous. I read in one place, I can't remember
    the exact source, but the United States is second only to China
    in terms of the number of video cameras that the average
    citizen encounters every day. The difference of course, being
    that most of the video cameras that we as Americans encounter
    are owned by individuals, are owned by the private sector.
        And so I think that perhaps in some communities that are
    more prone to gun violence, where some of the cameras belong to
    law enforcement versus other communities where they're held by
    individuals in the private sector. But I do think that right
    now, being under some form of camera surveillance is just an
    accepted part of life for all communities throughout the United
    States.
        Chair Booker. Thank you. Dr. Howard, I was really
    appreciative of your sort of best practices, and they resonated
    with Dr. Wexler in a significant way. And one of the things I
    think that resonates with Chief Aguilar is just this idea of
    training. Could you just go another level deeper on that? You
    said how important it's for analyst training. What does that
    mean?
        Dr. Howard. So we know that in order for these tools to be
    used properly, the analyst decisions before the algorithm comes
    into play, are critical, and the analyst decisions afterwards.
    So if we think about any one of these, if we think about a
    latent print analysis, for example, just to use a different
    one, the analyst, somebody in the human chain collects a
    fingerprint at a crime scene, or from a piece of evidence
    processes that fingerprint scans it.
        It decides whether it's of sufficient quality to even be
    able to match it to something else, or to attempt to link it to
    somebody else, marks it up, or has a computer program mark it
    up on where the key features of this fingerprint that might be
    worth comparing with a data base, puts it into the algorithm.
    All the algorithm is doing is comparing it to a data base of
    prints.
        There's nothing magical about that. That's been done for
    decades on computers before that, on cards stored in files.
    They would pull out the cards and they would compare each
    fingerprint. So all the algorithm is doing is speeding up that
    comparison process. And then at the end, it puts out a
    candidate list, and that candidate list is nothing more than
    the top 10, 20, 50, however many the program is designed for,
    or however many the analyst has asked for of the best matching
    candidates from the, you know, the best match at the top to the
    lesser matches as you move down the list.
        But that doesn't say anything about the strength of the
    actual evidence. The person at the top of the list might
    actually be a lousy match, but it's the best match in the data
    base, and that's all the algorithm is able to do. If you have a
    partial print, or a smeared print, or one that's distorted,
    they may not be able to get a very good match. And if you were
    putting a percent match on it, which is not what these
    algorithms do, but if you were doing that, it might say
    something like 35 percent match, and that's your number one
    candidate.
        Analysts often do not understand that. They often think
    this is my number one candidate, or these are my top 10. It's
    got to be one of these people that came up at the top of the
    list, and in reality, it might be somebody completely
    different. All the algorithm can do is find the best matches to
    whatever quality fingerprint they're given, and it has to be
    somebody who's already in the data base. And obviously, not
    everybody has their fingerprints in every data base that could
    be used for comparison. So a lot of human understanding at the
    beginning about what quality of print is good enough, and human
    understanding at the end about what do these results really
    mean.
        Chair Booker. Right. But variations in training for local
    police departments could vary widely and end up with widely
    different results.
        Dr. Howard. That's absolutely true.
        Chair Booker. And so that's why one of your recommendations
    is to have national standards. Right?
        Dr. Howard. We think national standards would help to drive
    the conversation if there were standards at the Federal level,
    or if there were standards that applied to how much is good
    enough. Right? What quality of print is good enough to put into
    a latent print algorithm?
        Some of those kinds of standards could start to bring more
    consistency to this and help reduce the potential for well-
    meaning human investigators who are doing their best to solve a
    crime, to be able to use the tools more effectively and
    interpret the results more efficiently.
        Chair Booker. Right. And how would you feel, is that overly
    prescriptive for the Federal Government? Because I'm a big
    believer having run a local police department, how cash
    strapped you are, how you're constantly battling for resources.
    And so things like cops grants and other grants for technology
    are really critical. But how would you feel if those grants
    came with a requirement for certain standards for your analysts
    and the like? Is that overly burdensome?
        Mr. Aguilar. Senator, I think that, you know, it's a bit of
    a broad question. But I think that just generally speaking, a
    grant that comes with a requirement that the agency receiving
    the grant properly train to a particular standard the people
    who are going to be carrying out the function, I don't think is
    overly burdensome.
        Chair Booker. Great. And so Dr. Howard, last question. When
    you say increased transparency, are you getting at some of the
    things that Professor Wexler's talking about in terms of the
    need of opening up these algorithms to peer review and to sort
    of a more adversarial analysis?
        Dr. Howard. So we looked at the criminal investigation
    portion, which I realize feeds into the courtroom, but we did
    not look at the courtroom processes that might occur. So I want
    to set the stage with that. But the experts we spoke with
    indicated that transparency is critical, both in terms of how
    the algorithm was built, what if it's an AI-enabled algorithm,
    what training data were used, but also then transparency about
    has it been tested. What is the accuracy of this through
    something like the NIST facial recognition vendor testing? What
    accuracy was determined through that kind of testing? What
    demographic bias did this algorithm exhibit through that kind
    of testing? That can be very useful information, and our
    experts were very much in favor of that.
        Opening the source code, though our experts told us it is
    not necessary. It is a path to figuring out how the algorithm
    works. It is not the only path in the view of the experts we
    spoke with. Independent third-party testing can accomplish many
    of the same things.
        Chair Booker. And you guys noted in your report that there
    was a lack of sufficient independent validation.
        Dr. Howard. That is often true. Yes. The NIST tests which
    are top of the line, gold standard testing, they are done only
    if a vendor chooses to submit its algorithm for testing.
    There's no requirement for a vendor to do that.
        Chair Booker. And you're saying right now the DOJ, there's
    a lack--in the DOJ's engagement of this technology right now,
    there's a lack of sufficient independent sort of oversight.
        Dr. Howard. So we didn't look at the DOJ in depth in terms
    of how they validate their algorithms. We did talk to a number
    of officials at FBI, and they told us--and I believe this is in
    our report--that they worked with NIST to develop a validation
    protocol that they then run in-house.
        Chair Booker. But you haven't evaluated that.
        Dr. Howard. We have not evaluated it, no. That was not part
    of the scope of our work. It's work we could do if there was
    interest.
        Chair Booker. Well, I have interest, but I can't speak for
    the DOJ. But if you were to be called upon to do that analysis,
    one of the things you would be looking at is sort of an
    independent objective review, a sufficient review of algorithms
    of the technology itself. Correct?
        Dr. Howard. Of the algorithm accuracy, demographic bias,
    features like that.
        Chair Booker. And what's the danger of perhaps them not
    having done that right now?
        Dr. Howard. Then they may not know how effectively their
    algorithm works, and they may not know that the candidate list
    they get out could be skewed by, for example, bias-training
    data that the algorithm was trained on. They just may not be
    aware of that if they haven't run that test.
        Chair Booker. Doesn't that set alarms? Should I be writing
    to the DOJ and saying, ``Look, I have legitimate concerns that
    you have not tested this in a sufficient way.''
        Dr. Howard. I would say any law enforcement entity that's
    using an algorithm that has not been validated in a form that
    would be considered defensible should set off alarms.
    Absolutely.
        Chair Booker. Okay. And then finally, Ms. Wexler, again,
    you traveled the farthest and I'm grateful. But I do wonder,
    you obviously have a lot of concerns in your testimony, but
    there are also protections that AI tools could use. I always
    think of these waves of technology as potentially democratizing
    because you talked about the possible expense of a defense
    attorney in a situation, but AI might be a tool that defense
    attorneys could use to help out.
        You've done a lot of writing and obviously sit with the
    technology group at Boalt. Are there some things that give you
    hope when you look at this technology that you might want to
    put into the record?
        Professor Wexler. Oh, yes, absolutely. To be clear, my
    concerns are neutral about the technology itself. I think AI
    technology is have potential to increase accuracy and efficacy
    of law enforcement investigations and prosecutions, and also
    have the potential to help criminal defense investigators
    identify evidence of innocence.
        So the technologies themselves are not the issue. It's the
    legal rules that we set up around them to help us ensure that
    they are the best, most accurate, and effective tools, and not
    flawed or fraudulent in some way.
        Chair Booker. I just want to say before I give my sort of
    closing remarks. Number one, I'm just so grateful to Senator
    Cotton with a lot of demands given some of the international
    issues that are going on for him to be here and give such, I
    thought, a really important line of questioning. His
    partnership is extraordinary.
        But I want to thank the three witnesses. It is a frontier
    that we have that I think it's difficult to anticipate where we
    will be in 5 years from now, and this technology is really
    accelerating.
        The challenge for government, and I've seen this in waves
    of innovation, is that we have not moved as fast as the
    innovations around us. I've seen this in nuclear energy. I've
    seen this in drone technology. The government has not been able
    to keep up. Maybe a platform of the substantive accord that we
    have in a bipartisan way on some of the challenges with social
    media, for example, is another great example.
        You-all are on the cutting edge of looking at this. And
    it's exciting to me not only to hear your testimony, but to
    hear what some common-sense precautions could be on a federal
    level, as well as ways to try to figure out how to advance the
    technology to the greatest aims of humanity, which is a
    democratic system that protects the rights of all individuals,
    including rights to privacy, as well as what I think is a
    fundamental human right or a freedom that we should have; which
    is a freedom from fear, a freedom from the kind of depraved
    criminality that we often see manifesting in communities as
    well.
        This testimony's been rich, both your written testimony and
    your verbal testimony. It gives me a lot of gratitude. I know
    that Congress as a whole is looking at AI from every different
    perspective. This is, from what I know of, the first hearing in
    the Senate that really is focusing in on. It's affecting the
    criminal justice system.
        My hope is that you-all will be available in the future to
    consulting with us. There may be some potential for some great
    bipartisan ideas to come out of this, or at the very least,
    opening up conversations with some of the key actors in law
    enforcement on the federal level. I cannot tell you, Chief
    Aguilar, as a guy who lives in a community that has struggled
    with a lot of the issues that yours has, the heroic work that
    you're doing every single day. It's just something that means a
    lot to me, and I look forward to visiting it hopefully. And the
    truth of the matter is I'm a little concerned that we'll be
    confused for each other because we're both--you know, have
    similar haircuts. But I'm hoping that that won't be the case.
        But I want to remind everyone that the Subcommittee
    questions for the record are due a week from today, Wednesday,
    January 31 at 5:30 p.m., and I hope that, should there be
    questions, because again, the Senators have been pulled in so
    many different directions. My Ranking Member's team might have
    some questions for the record as well. I hope that you-all
    after all the sacrifice you made, getting here, preparing
    written testimony, and testifying, that you will still be able
    to respond in a timely fashion to any questions that are sent
    to you.
        When you testify before the U.S. Senate, I know how
    important that is. It may seem like a small Subcommittee
    hearing, but it is a service to your country, and the work that
    you-all are doing to me stands in that stead of patriotism, of
    giving to a nation, helping us to be better. Today was very,
    very helpful, and I'm grateful to each of you individually for
    participating and being a part. With that, the Subcommittee is
    adjourned.
        [Whereupon, at 4:02 p.m., the hearing was adjourned.]
        [Additional material submitted for the record follows.]

                                A P P E N D I X

    Submitted by Senator Booker:

     CAIDP--AI and Criminal Prosecution Statement.....................    65

    [GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]

                                     [all]