Sharad Goel, Assistant Professor, Stanford Management Science & Engineering
Designing Equitable Risk Models for Lending and Beyond
From credit scoring to evaluating job candidates, statistical models are increasingly used to guide high-stakes human experts, including lenders, managers, judges, and doctors. Researchers and policymakers, however, have raised concerns that these machine-learned algorithms might inadvertently exacerbate societal biases. For example, poorly constructed credit scores might disproportionately harm minority borrowers. To measure and mitigate such potential bias, there's recently been an explosion of competing mathematical definitions of what it means for a risk model to be fair. But there’s a problem: nearly all the prominent definitions of fairness suffer from subtle shortcomings that can lead to serious adverse consequences when used as a design principle. I'll illustrate these problems that lie at the foundation of this nascent field of algorithmic fairness, drawing on ideas from machine learning, economics, and legal theory. In doing so, I hope to offer practitioners a way to make more equitable decisions.
Sharad is an assistant professor at Stanford in the Department of Management Science & Engineering, in the School of Engineering. He also has courtesy appointments in Computer Science, Sociology, and the Law School.
Sharad's primary area of research is computational social science, an emerging discipline at the intersection of computer science, statistics, and the social sciences. He's particularly interested in applying 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 Stanford, Sharad was a senior researcher at Microsoft Research and Yahoo Labs.
He is the founder and executive director of the Sta nford Computational Policy Lab, a team of researchers, data scientists, and journalists that addresses policy problems through technical innovation. In collaboration with the Computational Journalism Lab, they created the Stanford Open Policing Project, a repository of data on over 100 million traffic stops across the United States.
He often writes general-audience pieces about contemporary policy issues from a statistical perspective. These include discussions of algorithms in the courts (in the New York Times and the Washington Post); police stops (in Slate and The Huffington Post); election polls (in the New York Times); and claims of voter fraud (in Slate, and also an extended interview with This American Life).
He studied at the University of Chicago (B.S. in Mathematics) and at Cornell (M.S. in Computer Science; Ph.D. in Applied Mathematics). Before joining the Stanford faculty, he worked at Microsoft Research in New York City.