Stylish Risk-Limiting Audits in Practice
Amanda K. Glazer, Jacob V. Spertus, and Philip B. Stark

TL;DR
This paper presents an open-source Python implementation of risk-limiting audits that utilize contest-specific ballot card data to significantly reduce the number of ballots needing manual review, demonstrated on large U.S. elections.
Contribution
It introduces a practical, open-source tool for risk-limiting audits that leverages contest-specific ballot data to improve efficiency in real-world elections.
Findings
CSD-based RLAs reduced sample sizes to about 0.65% and 3.1% of total ballots in two large elections.
The method successfully audited all contests with minimal hand counting, including close races.
The software is demonstrated on elections with over 1.8 million voters.
Abstract
Risk-limiting audits (RLAs) can use information about which ballot cards contain which contests (card-style data, CSD) to ensure that each contest receives adequate scrutiny, without examining more cards than necessary. RLAs using CSD in this way can be substantially more efficient than RLAs that sample indiscriminately from all cast cards. We describe an open-source Python implementation of RLAs using CSD for the Hart InterCivic Verity voting system and the Dominion Democracy Suite(R) voting system. The software is demonstrated using all 181 contests in the 2020 general election and all 214 contests in the 2022 general election in Orange County, CA, USA, the fifth-largest election jurisdiction in the U.S., with over 1.8 million active voters. (Orange County uses the Hart Verity system.) To audit the 181 contests in 2020 to a risk limit of 5% without using CSD would have required a…
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Taxonomy
TopicsSports Analytics and Performance · Hate Speech and Cyberbullying Detection
