No—not by algorithms alone. Data science can help reveal when a proposed voting map produces unusually partisan outcomes compared with maps drawn under the same stated rules. It can also help commissions explore alternatives. But software cannot settle which rules should govern, guarantee that its inputs are neutral, or make an institution adopt a fair map. Ending gerrymandering requires enforceable criteria and decision-makers with the authority and incentive to follow them.
How can algorithms help detect gerrymandering?
A common approach is to generate an ensemble: a large set of alternative district maps that satisfy the same specified constraints. Analysts then compare a proposed map’s partisan outcomes with the range of outcomes across that set. If the proposed map is an outlier, that can be evidence that it deserves closer scrutiny.
The comparison is conditional, not a verdict. It tells you how a map looks relative to the maps the algorithm was able to generate under its chosen rules. It does not, by itself, prove why the map was drawn that way or establish a universal threshold between fair and unfair. Legal scholarship describes ensembles as a baseline for assessing possible political bias, not as a substitute for judgment (Emily Rong Zhang, 2021; Becker and Solomon, 2020 preprint record).
What an ensemble can and cannot show
- It can: compare a challenged map’s outcomes with plausible alternatives generated under declared constraints.
- It cannot, on its own: define fairness, establish intent, determine which criterion should take priority, or decide whether a map violates a particular law.
That distinction matters because a result can look extreme under one set of constraints and less unusual under another. The output is only as informative as the rules, data, and implementation behind it.
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Why not have a computer just draw a fair map?
A map-generating algorithm has to be told what to preserve and what to optimize. Some requirements come from federal or state law; others are policy choices. Population equality, geography, political boundaries, communities, compactness, and competitiveness can all affect which maps are produced. These goals can conflict, so there is no purely technical setting that makes every trade-off disappear.
For example, a rule that strongly prioritizes keeping political boundaries intact may produce a different map from one that gives more weight to compactness or competitiveness. The software can reveal consequences of those choices, but cannot provide a neutral answer to the question of which value should win. Scholarship on algorithmic redistricting likewise emphasizes that criteria and constraints shape the results (Georgetown Law Journal, 2023).
What should be disclosed to make an analysis useful?
- The criteria and constraints used, including which are legal requirements and which are discretionary.
- The data and implementation used to generate maps, with enough detail for others to assess or reproduce the analysis.
- How the proposed map compares with the alternatives produced under those inputs—and how the comparison changes when reasonable assumptions change.
- Who has authority to choose the final map and whether that decision-maker is institutionally independent.
Without that information, a polished chart or numerical score can obscure rather than resolve the key question: what choices produced this benchmark?
What does U.S. law allow courts to do about partisan gerrymandering?
For federal constitutional claims, a central limit comes from Rucho v. Common Cause. On June 27, 2019, the Supreme Court held that claims of excessive partisan gerrymandering are not justiciable in federal court under the U.S. Constitution because it found no judicially manageable standard for deciding when partisan influence has gone too far. Chief Justice John Roberts wrote: “The fact that the Court can adjudicate one-person, one-vote claims does not mean that partisan gerrymandering claims are justiciable.” Read the Court’s opinion in Rucho.
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The ruling also does not erase federal requirements concerning equal population or racial gerrymandering. In Alexander v. South Carolina State Conference of the NAACP, decided May 23, 2024, the Court reiterated that a map drawn to achieve a partisan end is not, for that reason alone, actionable as a partisan-gerrymandering claim in federal court. A racial-gerrymandering claim can trigger strict scrutiny if race predominates in drawing the map. When race and party preference correlate, a central legal and evidentiary issue is distinguishing racial motivation from partisan motivation. See the Court’s opinion in Alexander.
Which approach puts algorithms to the most useful use?
Algorithms can support decision-making without being given the final decision. An independent commission can use them to explore feasible maps, learn how criteria affect outcomes, and identify trade-offs while there is still time to discuss them. That differs from automatically selecting and adopting a map based on a computer’s score.
| Approach | What the algorithm does | What still determines legitimacy |
|---|---|---|
| Ensemble analysis | Compares a proposed map with alternatives generated under the same stated constraints. | Whether the constraints and implementation are disclosed and appropriate to the legal question. |
| Commission decision support | Helps commissioners explore feasible maps and understand trade-offs among criteria. | The commission’s authority, independence, membership, and process for choosing a plan. |
| Fully automated adoption | Selects or recommends a map according to rules encoded in the system. | Who set the rules, whether they comply with law, and which accountable body has power to adopt the map. |
Algorithmic support may help a commission work more transparently, but it does not make the commission independent or its members neutral by itself. The institution and its process remain part of the result (Zhang, 2021).
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What would it take to reduce gerrymandering in practice?
Algorithms are most useful as auditing and deliberation tools within a process that has clear rules and accountable decision-makers. A practical reform effort needs to answer three separate questions:
- What standards govern mapmaking? Specify which population, geographic, community, political-boundary, and other criteria apply, and how conflicts among them should be handled.
- How can the public assess the analysis? Disclose the inputs and constraints so people can understand what the comparison means and evaluate whether the benchmark is appropriate.
- Who can act on the result? Give a legislature, commission, or court authority under applicable law to review or adopt a map. In the partisan-gerrymandering context, the available legal route may be state rather than federal.
Software can make a map’s consequences harder to hide and help people compare alternatives. It cannot replace the political and legal choices that define fairness, or the institutions responsible for enforcing those choices.
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