The Census is doing something new and interesting this year: implementing a differentially private algorithm called TopDown, so named because it works its way down the levels of a geographic hierarchy. The Census is already facing lots of scrutiny of its ability to provide a reliable count for marginalized communities under unprecedented political pressure from the White House and in pandemic conditions. Will this privacy protection mechanism result in erasure and undercounting for the same hard-to-count groups? What are the impacts on redistricting and the enforcement of the Voting Rights Act? I'll describe joint work with Aloni Cohen, JN Matthews, Bhushan Suwal, and Peter Wayner, incorporating reconstruction data from Mark Hansen and Denis Kazakov. No prior knowledge of U.S. politics required.
Moon Duchin is a mathematician at Tufts University with affiliations in Science, Technology, and Society; Race, Colonialism, and Diaspora; and the Tisch College of Civic Life. She runs the MGGG Redistricting Lab at Tisch. Moon's mathematical background is in geometric group theory and low-dimensional topology. Her lab works on data science for civil rights, incorporating mathematics, computing, geography, law, and policy in its research program. Recent projects include gerrymandering metrics, Voting Rights Act analysis, and disclosure avoidance in the Census.