Building 200, Room 303
450 Serra Mall
Stanford, CA 94305
Machine learning algorithms are increasingly used to guide decisions by human experts, including judges, doctors, and managers. Researchers and policymakers, however, have raised concerns that these systems might inadvertently exacerbate societal biases. Dr. Goel will discuss different ways to conceptualize, design, and evaluate equitable algorithms, drawing on ideas from computer science, economics, and legal theory.
This talk is presented as a part of the Comm230X +1 Speaker Series, and is open to both Stanford students and the general public.
Sharad Goel is an Assistant Professor at Stanford in the Department of Management Science & Engineering, and is the founder and director of the Stanford Computational Policy Lab. He also holds courtesy appointments in Computer Science, Sociology, and the Law School. In his research, Sharad looks at public policy through the lens of computer science, bringing a new, computational perspective to a diverse range of contemporary social issues. He applies modern computational and statistical techniques to study social and political policies, such as stop-and-frisk, swing voting, filter bubbles, do not-track, and media bias. Before joining the Stanford faculty, Sharad completed a Ph.D. in Applied Mathematics at Cornell University, and worked as a Senior Researcher at Yahoo and Microsoft in New York City.