AI, Liability, and Hallucinations in a Changing Tech and Law Environment

Stanford Law researchers Professor Dan Ho and Mirac Suzgun explore the risks and potential of AI in law --- Since ChatGPT came on the scene, numerous incidents have surfaced involving attorneys submitting court filings riddled with AI-generated hallucinations—plausible-sounding case citations that purport to support key legal propositions but are, in fact, entirely fictitious. As sanctions against attorneys mount, it seems clear there are a few kinks in the tech. Even AI tools designed specifically for lawyers can be prone to hallucinations.  In this episode, we look at the potential and risks of AI-assisted tech in law and policy with two Stanford Law researchers at the forefront of this issue: RegLab Director Professor Daniel Ho and JD/PhD student and computer science researcher Mirac Suzgun. Together with several co-authors, they examine the emerging risks in two recent papers, “Profiling Legal Hallucinations in Large Language Models” (Oxford Journal of Legal Analysis, 2024) and the forthcoming “Hallucination-Free?” in the Journal of Empirical Legal Studies. Ho and Suzgun offer new insights into how legal AI is working, where it’s failing, and what’s at stake. Links: Daniel Ho  >>> Stanford Law page Stanford Institute for Human-Centered Artificial Intelligence (HAI) >>> Stanford University page Regulation, Evaluation, and Governance Lab (RegLab) >>> Stanford University page Connect: Episode Transcripts >>> Stanford Legal Podcast Website Stanford Legal Podcast >>> LinkedIn Page Rich Ford >>>  Twitter/X Pam Karlan >>> Stanford Law School Page Stanford Law School >>> Twitter/X Stanford Lawyer Magazine >>> Twitter/X   (00:00:00) Introduction to AI in Legal Education  (00:05:01) AI Tools in Legal Research and Writing (00:12:01) Challenges of AI-Generated Content  (00:20:0) Reinforcement Learning with Human Feedback (00:30:01) Audience Q&A Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.