Using Algorithms to Detect Gerrymandering and Improve Legislative Redistricting: Cases from the United States and Japan

You are kindly invited to attend the seminar “Using Algorithms to Detect Gerrymandering and Improve Legislative Redistricting: Cases from the United States and Japan” which will be presented by Prof Kosuke Imai, Harvard University, on Tuesday, 5^th November – 5.00pm at the Old Physics Theatre, Museum of Literature Ireland (MoLI, 86 St Stephen’s Green, D02 XY43)

Summary: In representative democracies, legislative redistricting, which redraws district boundaries after Census, plays a fundamental role in ensuring equal representation.  Redistricting also influences who is elected and hence what policies are eventually enacted.  Because the stakes are high, redistricting has been subject to intense political battles. Parties often engage in gerrymandering by manipulating district boundaries to amplify the voting power of some groups while diluting that of others.  Drawing upon my own involvement in actual redistricting court cases in the United States, I will discuss how computational algorithms, combined with granular data, can be used to detect gerrymandering.  I will also use these algorithms to evaluate the partisan bias of Japanese redistricting where politicians play less prominent roles than the United States.

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Speaker’s bio

Kosuke Imai (pronounced Ksk) is Professor in the (opens in a new window)Department of Government and the (opens in a new window)Department of Statistics at (opens in a new window)Harvard University. He is also an affiliate of the (opens in a new window)Institute for Quantitative Social Science where his office is located. Before moving to Harvard in 2018, Imai taught at (opens in a new window)Princeton University for 15 years where he was the founding director of the (opens in a new window)Program in Statistics and Machine Learning. Imai specializes in the development of statistical methods and machine learning algorithms and their applications to social science research. His areas of expertise include causal inference, computational social science, and survey methodology. Imai leads the (opens in a new window)Algorithm-Assisted Redistricting Methodology Project (ALARM) and served as an expert witness for several high-profile legislative redistricting cases. In addition, he is the author of (opens in a new window)Quantitative Social Science: An Introduction (Princeton University Press, 2017). Outside of Harvard, Imai served as the President of the (opens in a new window)Society for Political Methodology from 2017 to 2019.

His current research interests include: data-driven policy learning and evaluation, causal inference with high-dimensional and unstructured treatments (e.g., texts, images, videos, and maps), fairness and racial disparity analysis, algorithmic redistricting analysis, data fusion and record linkage, census and privacy.

During the academic year 2024-2025, I will be spending my sabbatical at (opens in a new window)Nuffield College, (opens in a new window)University of Oxford.