Adaptively Weighted Audits of Instant-Runoff Voting Elections: AWAIRE

TL;DR

This paper introduces AWAIRE, a risk-limiting audit method for IRV elections that adaptively learns hypotheses to efficiently verify outcomes without requiring cast vote records, applicable even in manual tabulation contexts.

Contribution

The paper presents AWAIRE, a novel adaptive audit method for IRV elections that works without cast vote records and improves efficiency when CVRs are available.

Findings

AWAIRE is likely to be efficient in practice based on simulations.

It can handle elections with up to six candidates.

The method is adaptable to elections with more candidates.

Abstract

An election audit is risk-limiting if the audit limits (to a pre-specified threshold) the chance that an erroneous electoral outcome will be certified. Extant methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot. CVRs are not always available, for instance, in jurisdictions that tabulate IRV contests manually. We develop an RLA method (AWAIRE) that uses adaptively weighted averages of test supermartingales to efficiently audit IRV elections when CVRs are not available. The adaptive weighting 'learns' an efficient set of hypotheses to test to confirm the election outcome. When accurate CVRs are available, AWAIRE can use them to increase the efficiency to match the performance of existing methods that require CVRs. We provide an open-source prototype implementation that can handle elections with up to six candidates. Simulations using data from real elections show that AWAIRE is likely to be efficient in practice. We discuss how to extend the computational approach to handle elections with more candidates. Adaptively weighted averages of test supermartingales are a general tool, useful beyond election audits to test collections of hypotheses sequentially while rigorously controlling the familywise error rate.