Algorithms, Race, and Redistricting: Can Computers Find Fairness?

Title: Algorithms, Race, and Redistricting: Can Computers Find Fairness?

Abstract: Today’s Supreme Court is unmistakably inclined to reject the use of race-conscious measures in law and policy — as Chief Justice Roberts memorably put it, “The way to stop discrimination on the basis of race is to stop discriminating on the basis of race.” The 2023 term saw high-profile challenges to the use of race data in college admissions and in political redistricting. On the gerrymandering front, the state of Alabama asked the court to adopt a novel standard using algorithms to certify race-neutrality, on the principle that computers don’t know what you don’t tell them. But do blind approaches find fairness? In this talk, Professor Duchin will review the very interesting developments of the last few decades — and the last few months! — on algorithms, race, and redistricting.

Bio: Moon Duchin is a Professor of Mathematics and a Senior Fellow in the Tisch College of Civic Life at Tufts University. Her pure mathematical work is in geometry, topology, groups, and dynamics, while her data science work includes collaborations in civil rights, political science, law, geography, and public policy on large-scale projects in elections and redistricting. She has recently served as an expert in redistricting litigation in Wisconsin, North Carolina, Alabama, South Carolina, Pennsylvania, Texas, and Georgia.  Her work has been recognized with an NSF CAREER grant, a Guggenheim Fellowship, and a Radcliffe Fellowship, and she is a Fellow of the American Mathematical Society. 

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Webinar ID: 970 8640 5738