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Election forensics: Using machine learning and synthetic data for possible election anomaly detection.

ABSTRACT: 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.

SUBMITTER: Zhang M 

PROVIDER: S-EPMC6822750 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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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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