Speaker: Elliott Ash

Institution: Law, Economics, and Data Science Group at ETH Zurich

Date: 14:00 BST, 2nd July, 2025

Talk Title: Courts of Tomorrow: Experimental Evidence on AI-Augmented Justice in Pakistan

Abstract: Legal AI is poised to transform the work of legal professionals. We report on a randomized field experiment conducted in partnership with Pakistan’s Federal Judicial Academy, in which we integrated AI technology and skills into the workflow of Pakistan judges. We developed an LLM-based AI support tool designed to assist judges in their work, including a RAG system facilitating search, citation, and summarization of Pakistani legal precedents. Almost one-half of Pakistan’s trial court judges signed up for the study and were randomized into three waves, where they received access to this AI tool along with a nine-hour training course in its capacities and limitations. In a fourth placebo wave, judges received training on legal technologies other than AI. We demonstrate high and persistent take-up of the tool among treated judges. We then evaluate the effects of AI tools and training on judge work satisfaction, case disposal rates, and the quality of written judgments. These results provide insights into the potential of generative AI to enhance judicial efficiency and state capacities globally.

Bio: Elliott Ash is Associate Professor of Law, Economics, and Data Science at ETH Zurich’s Center for Law & Economics, Switzerland. Prior to joining ETH, Elliott was Assistant Professor of Economics at University of Warwick, and before that a Postdoctoral Research Associate at Princeton University’s Center for the study of Democratic Politics. He received a Ph.D. in economics and J.D. from Columbia University, a B.A. in economics, government, and philosophy from University of Texas at Austin, and an LL.M. in international criminal law from University of Amsterdam. Elliott’s research and teaching focus on empirical analysis of the law and legal system using techniques from econometrics, natural language processing, and machine learning. His research has been published in American Economic Journal: Applied Economics, American Economic Journal: Economic Policy, Journal of Law and Economics, Annual Review of Economics, Economic Journal, Cornell Law Review, Georgetown Law Journal, Journal of Public Economics, Journal of Politics, and Political Analysis. Elliott’s research has earned grant funding from the European Research Council, Swiss National Science Foundation, Swiss Data Science Center, U.S. National Science Foundation, the Turing Institute, and the Washington Center for Equitable Growth.