Legal Text Analytics and Empirical Legal Studies
Program 5: Friday, March 11, Noon to 1:30 p.m. CT
University of Pittsburgh Professor Kevin Ashley organized the March 11 program.
March 11 Speakers, Abstracts, and Bios
Large Language Models and the Law
Daniel E. Ho, Stanford University
Abstract: This talk will discuss the emergence of large language models, their applicability in law and legal research, and the legal issues raised by the use of such models. We will illustrate with the CaseHOLD dataset, comprised of over 53,000+ multiple choice questions to identify the relevant holding of a cited case, and the application to mass adjudication systems in federal agencies.
Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law at Stanford Law School, Professor of Political Science, and Senior Fellow at the Stanford Institute for Economic Policy Research. He is also Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence, Faculty Fellow at the Center for Advanced Study in the Behavioral Sciences, and is Director of the Regulation, Evaluation, and Governance Lab (RegLab). He received his J.D. from Yale Law School and Ph.D. from Harvard University and clerked for Judge Stephen F. Williams on the U.S. Court of Appeals, District of Columbia Circuit.
Reading (Judges') Minds with Natural Language Processing
Elliott Ash, ETH Zurich
Abstract: This talk will introduce some recent lines of empirical legal research that apply natural language processing to analyze beliefs and attitudes of judges and other officials. When do lawmakers use more emotion, rather than logic, in their rhetoric? When do judges use notions of economic efficiency, rather than fairness or justice, in their written opinions? What can language tell us about political views or social attitudes?
Elliott Ash centers his research around the possibility and problem of "building a robot judge." As a professor at ETH Zurich's Center for Law & Economics, Elliott investigates the workings of law and policy through the lens of data science. Using natural language processing to sift through legal texts, and with natural experiments to get at causation, this research produces evidence to better understand how legal decisions are made. In the future, this work will provide a framework to support fairer decisions. Prior to joining ETH, Elliott held academic positions at University of Warwick and Princeton University, and before that earned a Ph.D. in economics and a J.D. from Columbia University.
Contact Professor Kevin Ashley for a Zoom link.