Sharad Goel, Stanford University
Statistical and algorithmic methods are increasingly used throughout the criminal justice system, from predictive policing to sentencing. I’ll discuss two recent applications of this approach: (1) real-time risk assessments for stop-and-frisk and for bail decisions; (2) tests for discrimination in traffic stops. For the former, I’ll argue that simple, statistically informed heuristics can outperform expert human decision makers in both efficiency and equity. For the latter, I’ll show that popular tests for racial bias suffer from subtle flaws, and will propose a new method for measuring discrimination.
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Sharad Goel is an Assistant Professor at Stanford in the Department of Management Science and Engineering, and, by courtesy, Computer Science and Sociology. He draws on a combination of methods from computer science and statistics to study contemporary issues in public policy, including police practices, collective decision-making, voter laws, media bias, and online privacy. Prior to joining the Stanford faculty, Sharad was a Senior Researcher at Microsoft.