{shortcode-500b4f329575d6e22184488538c4bba52c096ff2}

Redist — a tool developed by Harvard undergraduates, graduate students, and faculty — could impact the fate of a Supreme Court case involving allegations of racial gerrymandering in Alabama.

The software application, which officially rolled out in 2020, uses statistics to identify gerrymandering in electoral districts, providing empirical evidence that can be introduced into court cases evaluating partisan or racial gerrymandering.

Kosuke Imai, a professor of government and statistics at Harvard, led the developer team in collaboration with American Civil Liberties Union data scientist Ben H. Fifield. The team also included Harvard Graduate School of Arts and Sciences doctoral students Cory W. McCartan and Christopher T. Kenny, who developed a tool that runs simulations to determine whether a redrawn district is statistically abnormal.

The redist method differs from other gerrymandering identification tools, which typically compare outcomes between states without taking into account their differing political landscapes. The redist software prioritizes certain goals — like keeping counties together — in the drawing of district maps.

The algorithm is currently being used in Merrill v. Milligan, a lawsuit before the Supreme Court alleging Alabama’s 2021 congressional redistricting plan discriminates against Black voters.

Imai said the use of his tool in the Supreme Court case was surreal, as he typically produces work for use in academia, not the courtroom.

“When I saw the Supreme Court justices discussing our work, it was a moment where I never thought that would happen,” Imai said.

Fifield said the developers aim to “democratize” the software as much as possible, noting that redist is open source, meaning the code is available to anyone.

“A big part of the goal, especially with the open sourcing and making it more user-friendly, was so the general public could also use these tools and analyze redistricting plans,” Fifield said.

Though redist’s statistical approach was first introduced almost 10 years ago, Imai added that the algorithm has improved greatly since then, becoming “much more scalable” to the needs of medium and large states.

“The initial algorithms we developed were only applicable to very small states like New Hampshire,” Imai said. “It wasn't very applicable to medium or larger-sized states.”

The team also adapted the program to better visualize the simulations it runs.

“Chris Kenny and Tyler Simko both contributed to developing packages, software packages, which is really important because in order to use these algorithms in the court or the other settings you have to be able to communicate the results,” Imai said.

The team first applied this scaled-up version of redist to the 2020 elections. Currently, the team is analyzing the evolution of electoral districts since 2010.

Imai said he hopes to continue expanding analysis beyond the realm of U.S. elections. Already, the team is looking into applying the tool to international elections.

“Another project, with actually another undergraduate, evaluated in Japan, so we made a presentation to their national redistricting commission about some of our findings,” Imai said.

Imai said he was encouraged about redist’s impact on the future of election policy and redistricting in the United States.

“This really gave me the opportunity to be part of real-world policymaking, and I’m hoping that more opportunities like this will come along as the government and private entities start using our data and making data-driven policies,” he said.