- Thank you, Jennifer.
So we decided that giving one speaker five minutes
was too much, so this next five minute slot
will be two speakers, David Engstrom and Dan Ho.
David is a Professor of Law and Associate Dean
and the Bernard D. Bergreen Faculty Scholar
in the Stanford Law School.
And Dan is the William Benjamin Scott
and Luna M. Scott Professor of Law,
a Professor of courtesy of Political Science,
and a Senior Fellow
at the Stanford Institute for Economic Policy Research.
- We're here to talk about two collisions.
The first is this.
These are our students.
15 are law students and 10 are computer science PhDs,
and their worlds are colliding
in an interdisciplinary project
we're co-leading at the law school,
advising the Federal Government
on the use of AI by administrative agencies.
So think the SEC or the EPA.
Here's the second collision.
On the one hand, the body of law
that governs agencies is premised on transparency,
accountability, and reason-giving.
When government takes action, it affects our rights.
The Constitution and due process say it must explain why.
On the other hand, the AI tools
that government agencies are increasingly using
are not yet, at least, fully explainable.
We want to make the case
that if law and the AI community can work together
to make these collisions productive rather than destructive,
we can modernize government in ways
that are legal, ethical, and humane.
- Exhibit A, the Social Security Administration is
the largest adjudication agency in the Western world.
Some 1,600 judges make over 500,000 decisions
in disability benefits cases annually,
yet such mass adjudication faces serious problems.
First, there's a disturbing arbitrariness in grant rates
when cases are randomly assigned.
This figure depicts grant rates across judges in one region,
with one judge granting benefits
8% of the time and another granting benefits 98% of the time
for the same types of cases, leaving some to decry this
as a system of disability roulette.
Second, due process is resource-intensive.
Here's the file room
from another mass adjudicatory agency for veterans' claims,
where it takes an average of seven years
to resolve a veteran's appeal.
With our student team, we're studying the ways
to improve this state of affairs.
One success story comes
from the former head of the SSA Appeals Council
who hired lawyers who could code.
They developed a machine-learning model
that identifies cases
in which benefits should clearly be granted,
thus eliminating the need
for a resource and time-intensive hearing.
And another program uses natural language processing
to catch errors in draft decisions by judges,
such as in this pop-up
to please reevaluate at particular issue.
NLP may someday draft decisions more accurately
than the status quo.
These developments are interesting
not just as an application of AI,
but because they could shift
our understanding of the Constitution itself.
The Supreme Court once articulated two rationales
for the right to a hearing under the Constitution,
accuracy and dignity,
but the last 50 years have seen a fixation on accuracy.
AI can improve accuracy, but it can also free up resources,
allowing us to reclaim
the lost Constitutional rational of dignity.
As one judge told us,
litigants regularly appear in front of her and say,
I know that I will lose, but I just want to be heard.
- Exhibit B, enforcement is critical to governance.
Too little of it means costly law-breaking,
but going after the wrong people is also costly and unfair.
And so agencies are developing algorithmic tools
to help predict environmental violations, health care fraud,
and even which of you will cheat on your taxes next month.
(audience laughing)
And some of these tools are quite advanced.
The SEC is using NLP
to parse unstructured narrative disclosures like these
to predict which brokers are violating the securities laws.
But SEC officials make an interesting point.
The line level prosecutors
who actually have to bring enforcement actions,
they're not impressed
by a model's classification of a broker as high risk.
They want to know which part of the disclosure
triggered that classification and why.
And courts want to know why as well.
If a judge doesn't get
a sufficiently reasoned explanation from an agency,
the judge can void the agency's action.
Here is where the law presents
unique opportunities to AI researchers.
State-of-the-art NLP techniques work
quite well on short texts, but not nearly so well
on long, complicated, and jargony legal opinions.
But note that finding the bad apples
in narrative disclosures
is as much a human challenge as it is a technical one.
What is technically feasible,
legally permissible, and bureaucratically viable
will all have to be worked out in tandem.
We can do this, but only if law and computer science
can collide in the right ways.
- Modernizing the administrative state will require
both adapting AI and adapting the law.
The stakes are high because trust in government
is at an all-time low.
Done poorly, we risk fueling resentment
of faceless bureaucrats, deepening democratic distrust.
Done well, we can build a more effective
and human-centered system of law and government.
Thank you.
(audience applauding)