Election forensics: Using machine learning and synthetic data for possible election anomaly detection.
Ontology highlight
ABSTRACT:
SUBMITTER: Zhang M
PROVIDER: S-EPMC6822750 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Election forensics: Using machine learning and synthetic data for possible election anomaly detection.
Zhang Mali M Alvarez R Michael RM Levin Ines I
PloS one 20191031 10
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-lev ...[more]
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