While (the option of) replacing a judge with a robot is still many years away, the technological trends that will lead to this eventuality have been in motion for decades.
As early as the 1970s, computerized legal research services began to transform legal practice. Automated search through legal databases eventually gave way to automated search through digital document evidence. And most recently, legal automation is taking over contract review and drafting. In the courts, we have even begun to see the use of algorithms in decisions about whether to grant bail or parole.
This paper discusses the prospects for automating decisions in the legal system. I will discuss active research on decision prediction models for judges and prosecutors and how these algorithms might be used to detect and reduce bias in legal decision-making. I will also discuss the substantial risks for these algorithms to replicate existing biases in the system or create new ones. Along the way, I will discuss the role that incentives theory and econometrics can play in understanding and mitigating these risks.
This paper was written by Elliott Ash, Assistant Professor of Economics at University of Warwick in conjunction with the SMF and CAGE briefing event of the same name.
About the author
Elliott Ash is Assistant Professor of Economics at University of Warwick, where he teaches political economy and public finance. Elliott’s research focuses on empirical analysis of the law and legal system using techniques from applied microeconometrics, natural language processing, and machine learning. Elliott was previously a Postdoctoral Research Associate at Princeton University’s Center for the Study of Democratic Politics. He received a PhD in economics and JD from Columbia University, a BA in economics, government, and philosophy from University of Texas at Austin, and an LLM in international criminal law from University of Amsterdam. Meanwhile, Elliott has provided expert witness testimony for the Department of Justice Civil Rights investigation into discriminatory practices at Ferguson Police Department.