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'Fairmandering' Draws Fair Districts Using Data Science
It’s almost impossible for humans to draw unbiased maps, even when they’re trying. A new mathematical method developed by Cornell researchers can inject fairness into the fraught process of political redistricting – and proves that it takes more than good intent to create a fair and representative district. The two-step method, described in the paper, “Fairmandering: A Column Generation Heuristic for Fairness Optimized Political Districting,” first creates billions of potential electoral maps for each state, and then algorithmically identifies a range of possibilities meeting the desired criteria for fairness.