Redistricting from the Bottom Up: Sampling Communities of Interest with Differential Privacy
For redistricting commissions, this work provides a method to incorporate public input while mitigating adversarial manipulation, though it is domain-specific and incremental.
The authors propose using differential privacy to sample redistricting plans that incorporate community of interest (COI) testimonies, demonstrating that their method outperforms uninformed baselines and the enacted plan on Missouri data with 808 testimonies, while also showing robustness to adversarial attacks under certain privacy budgets.
Independent Redistricting Commissions (IRCs) are a promising tool for bottom-up redistricting, but their public testimony processes are vulnerable to adversarial manipulation. We propose using differential privacy to draw redistricting plans that incorporate community of interest (COI) testimonies while remaining robust to adversarial input. Treating individual testimonies as data points, we use the marked edge walk to sample from differentially private distributions of redistricting plans via the exponential mechanism. We introduce two score functions and demonstrate that both can be targeted by MEW across a range of privacy budgets. Applying this method to Missouri's mid-cycle redistricting using 808 COI testimonies, we show that COI-informed sampling outperforms an uninformed baseline and the enacted plan. An adversarial experiment demonstrates that the method can be robust to attacks under certain privacy budgets and may perform better in practice than formal group privacy guarantees imply. We also find that stronger COI preservation tends to spread minority and Democratic representation more evenly across districts.