Adaptively Weighted Audits of Instant-Runoff Voting Elections: AWAIRE

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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.

Original languageEnglish
Title of host publicationElectronic Voting
Subtitle of host publication8th International Joint Conference, E-Vote-ID 2023, Proceedings
EditorsMelanie Volkamer, David Duenas-Cid, Peter Rønne, Peter Y.A. Ryan, Jurlind Budurushi, Oksana Kulyk, Adrià Rodriguez Pérez, Iuliia Spycher-Krivonosova
Place of PublicationCham Switzerland
PublisherSpringer
Pages35-51
Number of pages17
Edition1st
ISBN (Electronic)9783031437564
ISBN (Print)9783031437557
DOIs
Publication statusPublished - 2023
EventInternational Joint Conference on Electronic Voting 2023 - Luxembourg City, Luxembourg
Duration: 3 Oct 20236 Oct 2023
Conference number: 8th
https://e-vote-id.org/programme-2023/
https://doi.org/10.1007/978-3-031-43756-4 (Proceedings)

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14230 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Joint Conference on Electronic Voting 2023
Abbreviated titleEVOTE-ID 2023
Country/TerritoryLuxembourg
CityLuxembourg City
Period3/10/236/10/23
Internet address

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