Tuesday, September 14, 2021 - 4:00pm to 5:00pm
Event Calendar Category
Machine learning methods are increasingly used to model risk in criminal justice, banking, healthcare, and other high-stakes domains. These new tools promise gains inaccuracy, but also raise challenging statistical, legal, and ethical questions. In this talk, I’ll describe the dominant axiomatic approach to fairness in machine learning, and argue that common mathematical definitions of fairness can, perversely, lead to discriminatory outcomes in practice. I’ll then present an alternative, consequentialist perspective for designing equitable algorithms that foregrounds the inherent tension between competing concerns in many real-world problems.
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 law school. He holds a bachelor’s degree in mathematics from the University of Chicago, as well as a master’s in computer science and a doctorate in applied mathematics from Cornell University.