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Please join us on Tuesday, April 21 at 12:00pm for a lecture by Simone Zhang, Assistant Professor of Sociology at New York University.
This talk is part of a symposium series presented by the UC Berkeley Computational Research for Equity in the Legal System Training Program (CRELS), which trains doctoral students representing a variety of degree programs and expertise areas in the social sciences, computer science and statistics. The talk is co-sponsored by the Berkeley Economy and Society Initiative (BESI) Tech Cluster, the Department of Sociology, the Algorithmic Fairness and Opacity Group (AFOG), the Criminal Law and Justice Center, and the Center for the Study of Law and Society.
Abstract
Risk assessment algorithms are increasingly adopted to improve criminal court decisions, yet their consequences remain contested. This talk presents findings from two studies leveraging a randomized controlled trial of the Public Safety Assessment (PSA) — a popular pretrial risk assessment tool — that randomized access to risk reports across cases.
In the first study, I combine transcripts of court hearings with decision outcomes to examine how risk scores shape bail decisions. When courts had access to the PSA, judges imposed cash bail at higher rates when the tool recommended it, with risk assessments serving as a rhetorical resource to justify harsher conditions.
In the second study, I investigate whether the PSA’s influence on proceedings alters how the public perceives court decisions. I randomly assigned U.S. adults to read transcripts from hearings conducted with and without the PSA. Although the PSA shifted decision-making, participants rated algorithm-assisted and algorithm-unassisted decision-making as comparable in quality, fairness, and trustworthiness. Taken together, these findings suggest that algorithmic tools can alter the punitiveness of pretrial decisions without diminishing or enhancing public perceptions of courts, complicating prospects for public accountability.
About the Speaker
Simone Zhang is an Assistant Professor of Sociology at New York University and a CIFAR Azrieli Global Scholar. Her research examines how classification systems, predictive models, and AI shape the distribution of benefits, burdens, and recognition in society. Much of her work focuses on the implications of these systems for institutional decision-making in social policy, education, and law. She received a PhD in Sociology from Princeton University.
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