Abstract: As data science has broadened its scope in recent years, a number of domains have applied computational methods for classification and prediction to evaluate individuals in high-stakes settings. These developments have led to an active line of recent discussion in the public sphere about the consequences of algorithmic prediction for notions of fairness and equity. In part, this discussion has involved a basic tension between competing notions of what it means for such classifications to be fair to different groups. We consider several of the key fairness conditions that lie at the heart of these debates, and in particular how these properties operate when the goal is to rank-order a set of applicants by some criterion of interest, and then to select the top-ranking applicants.

The talk will be based on joint work with Sendhil Mullainathan and Manish Raghavan.

Bio: Jon Kleinberg is the Tisch University Professor in the Departments of Computer Science and Information Science at Cornell University. His research focuses on the interaction of algorithms and networks, and the roles they play in large-scale social and information systems. He is a member of the National Academy of Sciences and the National Academy of Engineering, and the recipient of MacArthur, Packard, Simons, Sloan, and Vannevar Bush research fellowships, as well awards including the Harvey Prize, the Nevanlinna Prize, and the ACM Prize in Computing.

Location: There will be a viewing party at the HP Auditorium, 306 Soda Hall.

Virutal Location: You can also view the talk virtually by joining the conference below.

Join Zoom Meeting: https://zoom.us/j/825892192

Meeting ID: 825 892 192

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For more information, and to view upcoming talks in the Foundations of Data Science – Virtual Talk Series, view the website here.