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

Zhang, Mali and Alvarez, R. Michael and Levin, Ines (2019) Election forensics: Using machine learning and synthetic data for possible election anomaly detection. PLoS ONE, 14 (10). Art. No. e0223950. ISSN 1932-6203. PMCID PMC6822750. https://resolver.caltech.edu/CaltechAUTHORS:20191105-103337302

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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.

Item Type:

Article

Related URLs:

URL	URL Type	Description
https://doi.org/10.1371/journal.pone.0223950	DOI	Article
https://doi.org/10.1371/journal.pone.0223950.s001	DOI	S1 File
https://doi.org/10.7910/DVN/YZRJWD	DOI	Data
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822750/	PubMed Central	Article

ORCID:

Author	ORCID
Alvarez, R. Michael	0000-0002-8113-4451

Additional Information:

© 2019 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: February 13, 2019; Accepted: September 30, 2019; Published: October 31, 2019. The authors thank Lucas Núñez and Julia Pomares for their work on related projects. Data Availability: The replication materials have been deposited in Dataverse (https://doi.org/10.7910/DVN/YZRJWD), and the documentation for the replication materials contains relevant information for accessing the original data. The electoral data used in the paper were obtained from Datos Argentina, the official Argentina government open data site. Official demographic statistics were downloaded from the website of the National Institute of Statistics and Census of Argentina (INDEC). Alvarez thanks the John and Dora Haynes Foundation for supporting his research in this area. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have declared that no competing interests exist.

Funders:

Funding Agency	Grant Number
John and Dora Haynes Foundation	UNSPECIFIED

Issue or Number:

PubMed Central ID:

PMC6822750

Record Number:

CaltechAUTHORS:20191105-103337302

Persistent URL:

https://resolver.caltech.edu/CaltechAUTHORS:20191105-103337302

Official Citation:

Zhang M, Alvarez RM, Levin I (2019) Election forensics: Using machine learning and synthetic data for possible election anomaly detection. PLoS ONE 14(10): e0223950. https://doi.org/10.1371/journal.pone.0223950

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No commercial reproduction, distribution, display or performance rights in this work are provided.

ID Code:

99672

Collection:

CaltechAUTHORS

Deposited By:

Tony Diaz

Deposited On:

05 Nov 2019 18:54

Last Modified:

10 Feb 2020 21:14

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