In every representative democracy, free and fair elections in which voters choose their representatives is the foundation of democratic health. This ensures every citizen has equal representation and access to the political process, while each individual’s vote is weighed the same.

Unfortunately, there are mechanisms like gerrymandering that hijack this process by harnessing the power of modern computer science to redraw districts every 10 years for partisan political gain. In other words, representatives choose their constituents, rather than the other way around. This process is surging across America today.

In 2017, for example, mathematics professor Jordan Ellenberg described in the New York Times how complex gerrymandering algorithms allowed state officials to ensure Republican domination of Wisconsin districts in almost all political circumstances. He conceded, “as a mathematician, I’m impressed.”

As a computer scientist, I too am impressed by this! Although impressive, it is a troubling turn for our democracy.

As a computer science professor and operations researcher, I seek to model election equity and have created algorithms to optimize solutions to achieve that rather than partisan political gain. That is, nonpartisan researchers can use the power of computer science to generate solutions to optimize this political process for fairness. The top-line outcome of recent research is that by combining two approaches, “fairmandering” and “multi-member districts” (MMDs), one can address both intentional and inherent gerrymandering.

In thinking about statewide districting, there are two natural metrics to consider — the overall share of the vote that party A received, and the overall share of the seats that party A received. One goal in modeling fairness is to establish districts in which there is a proportional outcome — when the expected seat share closely tracks against the expected vote share.

Fairmandering uses historical records from recent elections to model, census block by census block, the likelihood that a specific share of the votes in that block are for party A. As a result, for any proposed district, or collection of blocks, we can characterize the likelihood of party A winning that district. Thus, for any district plan, we also can capture the expected gap, or lack of proportional outcome, between seat share and vote share. The aim of fairmandering is to select a districting plan that minimizes this gap.

MMDs provide a mechanism to overcome fairmandering limitations. Suppose that each U.S. congressional district elects multiple representatives, ideally through non-winner-takes-all voting, as a replacement for winner-take-all with smaller, single-member district. For example, New York might have nine districts where each district elects three representatives, rather than the current 27 districts with one representative. The elections in each district might have each voter provide a ranked list of preferences among all of the candidates, and by a mechanism known as single transferable vote, could ensure that within that district the number of seats a party gains is roughly in line with the partisan split within that district. This still preserves local representation, while providing two important advantages: expected seats mirror the vote share, and partisan interests are narrowed

Importantly, there is precedent for this in America. In 1962, 41 state legislatures had MMDs, and even today, 10 state legislatures elect representatives for at least one chamber in such a manner. This MMD-proportional voting method would support political minorities while stopping partisans attempting to gerrymander — their power is significantly curtailed.

At the end of the day, policymakers and staffers who use algorithms, equations, math and computer science for this process already should be using fairmandering. Given the sacred duty of drawing congressional districts, which shapes policy outcomes for all citizens, creating algorithms for fairness, diversity, equal opportunity and equal choice should be the primary focus of these officials. Without these principles involved in the mapmaking process, we run the risk of further democratic erosion while American citizens continue to lose faith in this crucial job.

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