Today we’re joined by Sharad Goel, Assistant Professor in the management science & engineering department at Stanford.
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Sharad, who also has appointments in the computer science, sociology, and law departments, has spent the recent years focused on applying machine learning to better understand and improve public policy. In our conversation, we dive into Sharad’s non-traditional path to academia, which includes extensive work on discriminatory policing, including practices like stop-and-frisk, leading up to his work on The Stanford Open Policing Project, which uses data from over 200 million traffic stops nationwide to “help researchers, journalists, and policymakers investigate and improve interactions between police and the public.”
Finally, we discuss Sharad’s paper “The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning,” which identifies three formal definitions of fairness in algorithms, the statistical limitations of each, and details how mathematical formalizations of fairness could be introduced into algorithms.
Connect with Sharad!
- Paper: The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning
- The Problem of Infra-Marginality in Outcome Tests for Discrimination
- The Stanford Open Policing Project
- Stanford Computational Policy Lab
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“More On That Later” by Lee Rosevere licensed under CC By 4.0