"Computation and the Search for Fairness in Representative Democracy"
Moon Duchin, Associate Professor of Mathematics; Senior Fellow, Tisch College of Civic Life, Tufts University.
Samuel Weiss Evans, Science, Technology & Society Fellow, Harvard Kennedy School.
Sheila Jasanoff, Faculty Associate. Pforzheimer Professor of Science and Technology Studies, Harvard Kennedy School.
Co-sponsored by the Graduate School of Arts and Sciences and the School of Engineering and Applied Sciences.
Lunch is provided if you RSVP. via our online form before Thursday afternoon, September 5th
We‘ve all heard that algorithmic assistance is increasingly used for high-stakes decisionmaking, from policing and sentencing to medical diagnosis to resource allocation, sometimes with manifestly unjust results. Often, these critical conversations treat algorithms as consummate black boxes, either because the algorithms are proprietary or because they are built on such complicated neural networks (say) that their interpretability is severely limited. But what if we wrote the algorithms? Electoral redistricting is an excellent problem domain for this inquiry: it seems perfectly clear that something is wrong and unfair about the way gerrymandered maps divide people for the purposes of voting, but it's quite hard to locate the precise harm and even harder to reason about remedies. I'll use the case of redistricting to tell overlapping stories: model design; Constitutional logic; and metrics of fairness.
Moon Duchin is an associate professor of Mathematics and Senior Fellow in the Tisch College of Civic Life at Tufts University. She serves as director of the interdisciplinary program in Science, Technology, and Society and as collaborating faculty in the Department of Race, Colonialism, and Diaspora Studies. Her mathematical subfields are geometry, topology, group theory, and dynamical systems. Her current research focus is in the study of electoral redistricting in the U.S., using Markov chain Monte Carlo and other randomized algorithms to understand relationships between community, partisanship, race, and representation.
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