Audits of the 2020 American election show an accurate vote count

• 成果类型: Article
• 署名作者: Baltz, Samuel; Gonzalez, Fernanda; Guo, Kevin; Jaffe, Jacob; Stewart III, Charles
• 署名单位: Massachusetts Institute of Technology (MIT); Wellesley College; Stanford University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-11253
• DOI: 10.1073/pnas.2419633122
• 发表日期: 2025-05-20

摘要

After many elections, the accuracy of the vote count is assessed by retabulating a small percentage of ballots. These audits form one of the richest bodies of evidence regarding electoral legitimacy, which is particularly important in democracies where the accuracy of elections has been prominently questioned. In decentralized democracies such as the United States, however, there is tremendous variation in the conduct and reporting of audits, which are never compiled into one place to facilitate precise analysis of their results. Here, we introduce a nation-scale audit result dataset, which we use to estimate the error rate in vote counting during the 2020 U.S. election. The dataset includes all available postelection tabulation audits, spanning 856 regional governments across 27 states, with 71,702,471 individual votes and a further 1,210,528 ballots, including about 6.2% of all votes cast for Donald Trump and 6.9% of those cast for Joe Biden. We find that election audits shifted the net presidential vote count by only about 0.007%, with similarly minuscule errors across all major types of electoral contests. The construction of a nation-scale election audit dataset represents a novel approach to benchmarking electoral legitimacy and provides particularly direct and comprehensive evidence that Americans' votes were counted correctly in 2020.