Fragile Algorithms and Fallible Decision-Makers: Lessons from the Justice System
Algorithms (in some form) are already widely used in the criminal justice system. We draw lessons from this experience for what is to come for the rest of society as machine learning diffuses. We find economists and other social scientists have a key role to play in shaping the impact of algorithms, in part through improving the tools used to build them.
This paper is forthcoming in the Journal of Economic Perspectives. Thanks to Amanda Agan, Charles Brown, Alexandra Chouldechova, Philip Cook, Amanda Coston, Dylan Fitzpatrick, Barry Friedman, Jonathan Guryan, Peter Hull, Erik Hurst, Sean Malinowski, Nina Pavcnik, Steve Ross, Cynthia Rudin, Greg Stoddard, Cass Sunstein, Timothy Taylor, Heidi Williams and Morgan Williams for valuable comments, and to Kristen Bechtel, Megan Cordes, Ellen Dunn, Rowan Gledhill and Elizabeth Rasich for their assistance. All opinions and any errors are of course our own. Mullainathan thanks the University of Chicago Booth School of Business, and the Roman Family University Professorship, for financial support. The authors gratefully acknowledge support for the construction of the New York City algorithm discussed in the paper from the non-profit Criminal Justice Agency to the University of Chicago Crime Lab, and for support for this paper specifically from the Sloan Foundation and the Center for Applied Artificial Intelligence at the University of Chicago Booth School of Business. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.