Election forensics and machine learning

We recently published a new paper on election forensics in PLOS ONE, “Election forensics: Using machine learning and synthetic data for possible election anomaly detection.” . It’s a paper that I wrote with Mali Zhang (a recent PhD student at Caltech), and Ines Levin at UCI. PLOS ONE is an open access journal, so there is no paywall!

Here’s the paper’s 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.

This new PLOS ONE paper advances the paper that Ines and I coauthored with Julia Pomares, “Using machine learning algorithms to detect election fraud”, that appeared in the volume of papers that I edited, Computational Social Science: Discovery and Prediction. This is an area where my research group and some of my collaborators are continuing to work on methodologies to quickly obtain elections data and analyze it for anomalies and outliers, similar to our Monitoring the Election project. More on all of this soon!