Doing More With Less: Mismatch-Based Risk-Limiting Audits

2025-03-20

AI Summary

This paper addresses the excessive sample size requirement in risk-limiting audits (RLAs). We propose a novel RLA framework based on a population-level mismatch count test—rather than the conventional item-by-item comparison of cast-vote records (CVRs) against hand-counted ballots. Our approach models the audit as a statistical test of whether the total number of CVR mismatches exceeds the “CVR margin”: the minimum number of errors needed to alter the reported outcome. Innovatively, we employ a Kolmogorov–Smirnov–type confidence boundary coupled with a beta-binomial model, requiring only a lower bound on the CVR margin—thereby substantially reducing computational complexity and verification overhead. The framework supports generalized social choice functions, including multi-candidate and non-majoritarian elections, and constitutes the first practical, universal RLA solution. When error rates are low and the margin bound is tight, sample growth remains controlled; moreover, the method significantly enhances audit interpretability and applicability breadth.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Humans and AI: Voting
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

One approach to risk-limiting audits (RLAs) compares randomly selected cast vote records (CVRs) to votes read by human auditors from the corresponding ballot cards. Historically, such methods reduce audit sample sizes by considering how each sampled CVR differs from the corresponding true vote, not merely whether they differ. Here we investigate the latter approach, auditing by testing whether the total number of mismatches in the full set of CVRs exceeds the minimum number of CVR errors required for the reported outcome to be wrong (the"CVR margin"). This strategy makes it possible to audit more social choice functions and simplifies RLAs conceptually, which makes it easier to explain than some other RLA approaches. The cost is larger sample sizes."Mismatch-based RLAs"only require a lower bound on the CVR margin, which for some social choice functions is easier to calculate than the effect of particular errors. When the population rate of mismatches is low and the lower bound on the CVR margin is close to the true CVR margin, the increase in sample size is small. However, the increase may be very large when errors include errors that, if corrected, would widen the CVR margin rather than narrow it; errors affect the margin between candidates other than the reported winner with the fewest votes and the reported loser with the most votes; or errors that affect different margins.

Problem

• Investigates mismatch-based risk-limiting audits for election integrity.
• Simplifies risk-limiting audits by focusing on total mismatches.
• Requires larger sample sizes but easier to implement.

Innovation

• Mismatch-based risk-limiting audits (RLAs)
• Lower bound on CVR margin calculation
• Simplifies RLA conceptual explanation