Calculated Discrimination: Exposing Racial Gerrymandering Using Computational Methods

In Rucho v. Common Cause, the Supreme Court held that challenges to partisan gerrymanders presented a nonjusticiable political question. This decision threatened to discard decades of work by political scientists and other experts, who had developed a myriad of techniques designed to help the courts objectively and unambiguously identify excessively partisan district maps. Simulated redistricting promised to be one of the most effective of these techniques. Simulated redistricting algorithms are computer programs capable of generating thousands of election-district maps, each of which conforms to a set of permissible criteria determined by the relevant state legislature. By measuring the partisan lean of both the automatically generated maps and the map put forth by the state legislature, a court could determine how much of this partisan bias was attributable to the deliberate actions of the legislature, rather than the natural distribution of the state’s population. Rucho ended partisan gerrymandering challenges brought under the U.S. Constitution—but it need not close the book on simulated redistricting. Although originally developed to combat partisan gerrymanders, simulated redistricting algorithms can be repurposed to help courts identify intentional racial gerrymanders. Instead of measuring the partisan bias of automatically generated maps, these programs can gauge improper racial considerations evident in the legislature’s plan and demonstrate the discriminatory intent that produced such an outcome. As long as the redistricting process remains in the hands of state legislatures, there is a threat that constitutionally impermissible considerations will be employed when drawing district plans. Simulated redistricting provides a powerful tool with which courts can detect a hidden unconstitutional motive in the redistricting process