Election forensics: Using machine learning and synthetic data for possible election anomaly detection
Autoři:
Mali Zhang aff001; R. Michael Alvarez aff001; Ines Levin aff002
Působiště autorů:
Division of Humanities and Social Sciences, California Institute of Technology, Pasadena, CA, United States of America
aff001; Department of Political Science, University of California, Irvine, CA, United States of America
aff002
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0223950
Souhrn
Assuring election integrity is essential for the legitimacy of elected representative democratic government. Until recently, other than in-person election observation, there have been few quantitative methods for determining the integrity of a democratic election. Here we present a machine learning methodology for identifying polling places at risk of election fraud and estimating the extent of potential electoral manipulation, using synthetic training data. We apply this methodology to mesa-level data from Argentina’s 2015 national elections.
Klíčová slova:
Machine learning – Research integrity – Publication ethics – Argentina – Elections – Literacy – Forensics – Supervised machine learning
Zdroje
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