Ash, Elliott, Prof.

Biography

Elliott Ash is Assistant 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.

Research Overview

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.

Bibliography

See external pageelliottash.comcall_made.

Teaching

• Building a Robot Judge: Data Science for Decision-Making (Fall Semester)
  This course explores the automation of decisions in the legal system. We delve into the machine learning tools needed to predict judge decision-making and ask whether techniques in model explanation and algorithmic fairness are sufficient to address the potential risks.
  
  
• Natural Language Processing for Law and Social Science (Spring Semester)
  This course explores the application of natural language processing techniques to texts in law, politics, and the news media. Students will put these tools to work in a course project.
  
  
• Big Data for Public Policy (Spring Semester)
  This course provides an introduction to big data methods for public policy analysis. Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors.
  

Previous Lectures:

• Fiscal Policy and Inequality
  This course provides an introduction to the political economy of fiscal policy-making. We first analyze policy inputs, with a focus on how elections select and incentivize different types of policymakers. Second, we analyze major fiscal policy outputs: choices of taxes, public goods, tax evasion, and inequality. Methods are from economics and applied statistics.