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
doi: 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:

Argentina – Elections – Forensics – Literacy – Machine learning – Publication ethics – Research integrity – Supervised machine learning


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Článek vyšel v časopise

PLOS One


2019 Číslo 10