How journalists, researchers, and civil rights groups are using big data, which put gerrymandering on steroids, to easily and cheaply track redistricting
THE SUPREME COURT'S conservative justices ruled Thursday that the highest court doesn't have the power to address partisan gerrymandering … Tweets: @wired , @wired , and @samwangphd Tweets: @wired : The Supreme Court's recent ruling on gerrymandering doesn't mean all hope is lost for reversing the harms caused by it. Policymakers, as well as citizens, can use the same technologies that supercharged gerrymandering to help stop it. https://www.wired.com/... @wired : “Technology has enabled gerrymandering, but it is also going to enable policing gerrymandering in ways it hasn't in the past.” https://www.wired.com/... Sam Wang / @samwangphd : ...let's take a break to make an important point. Today's enormous public energy can be amped up by technology. Communities of interest can advocate for themselves. Data mavens can diagnose proposed maps and draw their own. This is going to need Big Data! https://www.wired.com/...
Context & Ripple Effects
The Supreme Court's ruling that federal courts have no role in policing partisan gerrymandering came with a pointed warning from the dissent: technology has made manipulated maps far more effective and durable than before. This piece is the counterpoint to that resignation — the same big-data machinery that let mapmakers draw districts with surgical precision is now cheap enough for journalists, researchers like Sam Wang, and civil rights groups to run themselves.
The arc matters because the data infrastructure was already built for offense: political data firms have spent years vacuuming up psychological and personal data to sway voters even after Cambridge Analytica. Turning those capabilities toward auditing redistricting inverts the direction of the asymmetry between mapmakers and map-watchers.
First-order effects
- Journalists, academic researchers, and civil rights groups gain the ability to detect and quantify gerrymandering in near real time and at trivial cost, replacing what used to require expensive expert litigation support.
- With federal judicial review off the table per the Court's ruling, public-facing data analysis becomes one of the few remaining levers for naming and shaming manipulated maps.
Second-order effects
- State-level mapmakers and the parties behind them now operate under permanent computational surveillance, raising the cost of aggressive district-drawing even where courts won't intervene.
- The success of data-driven redistricting audits feeds adjacent legal strategies — civil lawyers are already building playbooks against automated systems that harm the public, as seen in efforts to push back on algorithms denying basic services (litigation against automated systems) — creating a template for challenging maps through other forums.
Third-order effects
- If the pattern holds, redistricting becomes an arms race fought with algorithms on both sides: researchers are already proposing AI systems, paired with human input, to draw fairer electoral districts outright (AI-assisted district drawing) rather than merely critique existing ones.
- Structurally, accountability for democratic processes shifts away from courts and toward a standing ecosystem of data-literate watchdogs — a model of distributed, technology-enabled oversight that generalizes beyond gerrymandering to other automated decision-making.
The trend: Computational tools are democratizing oversight of political processes, letting under-resourced watchdogs match the analytical power once held only by well-funded political operators.