The future of AI and the legal field

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. Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to [email protected]. Episode Reference Links: Stanford Profile:Julian Nyarko Connect With Us: Episode Transcripts >>> The Future of Everything Website Connect with Russ >>> Threads / Bluesky / Mastodon Connect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / Facebook 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   Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.