Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In PloS one

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.

Zhang Mali, Alvarez R Michael, Levin Ines

2019