Join us on February 22 at 12pm for a talk by Sharad Goel, Professor of Public Policy at Harvard Kennedy School. 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.
Abstract
When estimating the risk of an adverse outcome, common statistical guidance is to include all available factors to maximize predictive performance. Similarly, in observational studies of discrimination, general practice is to adjust for all potential confounds to isolate any impermissible effect of legally protected traits, like race or gender, on decisions. I’ll argue that this popular “kitchen-sink” approach can in fact worsen predictions in the first case and yield conservative estimates of discrimination in the second. To illustrate these ideas, I’ll draw on examples from healthcare and criminal justice.
About the Speaker
Sharad Goel is a Professor of Public Policy at Harvard Kennedy School. He looks at public policy through the lens of computer science, bringing a computational perspective to a diverse range of contemporary social and political issues, including criminal justice reform, democratic governance, and the equitable design of algorithms. Sharad is the founding director of the Computational Policy Lab, an interdisciplinary team of researchers, data scientists, and journalists that use technology to drive social impact. Prior to joining Harvard, Sharad was on the faculty at Stanford University, with appointments in management science & engineering, computer science, sociology, and the law school. Sharad holds an undergraduate degree in mathematics from the University of Chicago, as well as a master’s degree in computer science and a doctorate in applied mathematics from Cornell University.
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