Today, the Supreme Court will hear oral arguments for Gill v. Whitford, in which the state of Wisconsin will argue that congressional redistricting practices are not subject to judicial oversight. At the core of this hearing is whether partisan gerrymandering—a tactic used by political parties to redraw congressional voting districts so that the voting power within those districts is weighted toward their own party—was used to steal the 2012 state elections in Wisconsin from Democrats. The ramifications of this decision will be felt by the entire country. The Supreme Court will be deciding whether or not federal courts have the ability to throw out district maps for being too partisan, which requires the justices to be able to articulate just what constitutes partisan gerrymandering in the first place. The practice of gerrymandering has been a thorn in the side of American democracy for most of our nation’s existence, but continues largely unabated due to the difficulty of defining the point at which a new congressional district can considered to be the result of partisan gerrymandering. Various solutions to America’s gerrymandering problem have been proposed over the years, but most of these have failed to gain traction. In September, however, a team of data scientists at the University of Illinois published a paper to little fanfare that offered a novel solution to America’s gerrymandering woes: Let an algorithm draw the maps.
Although the Illinois researchers, led by computer science professor Sheldon Jacobson, aren’t the first to propose using artificial intelligence as a solution to the redistricting process, they hope their new approach will be more accessible and fair than previous attempts at stopping gerrymandering with computation.
These computer scientists are motivated by the belief that data and algorithms will create transparency in the notoriously opaque redistricting process by exposing the inputs and parameters that led to redrawing a district a certain way. With these inputs and parameters exposed, data scientists hope this will hopefully incentivize a more equitable redistricting process. Yet they are also the first to acknowledge that the same algorithms that can create equity in congressional redistricting can also be used to gerrymander with unprecedented efficiency.