The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
FREE & OPEN TO THE PUBLIC; REGISTRATION REQUIRED; LIVESTREAM AVAILABLE (USE REGISTRATION LINK ON THIS PAGE FOR LIVESTREAM)
Assistant Professor, Department of Management Science & Engineering, School of Engineering, Stanford University
Founding Executive Director, Stanford Computational Policy Lab
Columbia Population Research Center
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About the Seminar
Speaker Sharad Goel (bio) is in the emerging field of computational social science, which sits at the intersection of computer science, statistics, and the social sciences. He is passionate about applying modern computational and statistical techniques to understand and improve public policy. At this CPRC seminar, Goel will address the widespread concern that high-stakes decisions—made both by humans and by algorithms—are biased against groups defined by race, gender, and other protected traits. He will show that even the most popular measures of algorithmic fairness suffer from deep statistical flaws and that algorithms designed to satisfy those measures can, perversely, harm the very groups they were designed to protect. To demonstrate these ideas, he will propose a class of risk-assessment algorithms used by judges nationwide when setting bail.