Dr. Sharad Goel is a professor of Management Science and Engineering, as well as a professor of Computer Science and Law at Stanford University. He is the founder and executive director of the Stanford Computational Policy Lab, where he uses advanced data science techniques to examine the effects of social and political policies, and how those policies might be improved upon. In this episode, we discuss the intractability of algorithmic fairness. We explore how decision systems are being used and implemented in unsettling ways, and the mathematical reasons that three common goals for achieving algorithmic fairness are mutually-exclusive. Transcript available at: https://www.ambercazzell.com/post/msp-ep28-sharadgoel APA citation: Cazzell, A. R. (Host). (2020, February 15). Algorithmic Fairness and Its Discontents with Sharad Goel [Audio Podcast]. Retrieved from https://www.ambercazzell.com/post/msp-ep28-sharadgoel