Please note: This distinguished lecture will take place in DC 1302 as well as livestreamed over Zoom.
Professor of Public Policy, Harvard Kennedy School
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 misleading estimates of discrimination in the second. I’ll connect these results to ongoing debates in criminal justice, healthcare, and college admissions.
Bio: 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.
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. He 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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