Law professor Julian Nyarko has drawn attention for his studies using large language models to investigate and improve legal education and explore AI’s biases. He hopes AI can become a reliable, always-on legal learning and assistance tool to lower costs and expand access to legal services. In one recent study, he asked a group of law professors to evaluate written answers to student questions. Three-quarters of the time, the professors preferred AI-generated answers to those of their human colleagues. “AI is good at law,” Nyarko says, the challenge now is to use it most effectively, he tells host Russ Altman in this episode of Stanford Engineering’s The Future of Everything podcast.
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Chapters:
(00:00:00) Introduction
Russ Altman introduces guest Julian Nyarko, a professor of law at Stanford University.
(00:02:31) Path into AI and Law
How Nyarko’s early work led him to legal AI.
(00:05:03) Law, Economics, and Computation
How Nyarko’s training, methods, and self-taught coding shaped his research.
(00:07:23) Building the LIFT Lab
Why a law professor started a lab.
(00:09:06) Evaluating Legal AI
How AI raises fundamental questions about what counts as good lawyering.
(00:10:22) Improving Legal Services
Using AI to work faster, reduce errors, and make informed decisions.
(00:10:57) Rethinking Legal Education
How AI may change the way future lawyers learn
(00:11:51) AI in Office Hours
How AI answers law students’ questions compared with human professors.
(00:15:18) Surprising Results
Why AI answers were often preferred
(00:16:16) What the Study Shows
The findings support AI tutoring, but don’t prove AI improves learning.
(00:18:48) Limits of One-Shot Answers
Why real teaching often depends on dialogue, clarification, and productive struggle.
(00:20:59) AI for Social Science
How AI can become both an object of study and a tool.
(00:22:43) Research Agents
Using AI to test claims and make previously impossible research scalable.
(00:24:51) Agentic AI in the Lab
How Socratic dialogue with AI can sharpen research ideas.
(00:26:07) Fairness and Bias
How computational tools can be audited for bias and used to audit decision-making.
(00:27:29) Discrimination in Models
Exploring bias and how it can be reduced.
(00:30:16) Disparate Impact
How policies and systems disadvantage groups even without explicit intent.
(00:32:53) From Evidence to Policy
How Nyarko’s lab works with stakeholders to surface disparities.
(00:34:49) Future In a Minute
Rapid-fire Q&A: justice, talent, and the future of legal AI.
(00:36:57) Conclusion
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