An informed forensics approach to detecting vote irregularities

Jacob M. Montgomery, Santiago Olivella, Joshua D. Potter, Brian F. Crisp

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Electoral forensics involves examining election results for anomalies to efficiently identify patterns indicative of electoral irregularities. However, there is disagreement about which, if any, forensics tool is most effective at identifying fraud, and there is no method for integrating multiple tools. Moreover, forensic efforts have failed to systematically take advantage of country-specific details that might aid in diagnosing fraud. We deploy a Bayesian additive regression trees (BART) model-a machine-learning technique-on a large cross-national data set to explore the dense network of potential relationships between various forensic indicators of anomalies and electoral fraud risk factors, on the one hand, and the likelihood of fraud, on the other. This approach allows us to arbitrate between the relative importance of different forensic and contextual features for identifying electoral fraud and results in a diagnostic tool that can be relatively easily implemented in cross-national research.

Original languageEnglish (US)
Article numbermpv023
Pages (from-to)488-505
Number of pages18
JournalPolitical Analysis
Issue number4
StatePublished - Oct 2015
Externally publishedYes

ASJC Scopus subject areas

  • Sociology and Political Science
  • Political Science and International Relations


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