An Algorithm for Detecting Election Fraud

For better or worse, one of the biggest stories in US politics today is the detection of election fraud, or in many cases the lack of election fraud. But determining whether fraud happened in an election can be difficult, even while proving the validity of elections for some has become increasingly important. Wouldn’t it be incredible if we could just plug a set of data from an election into a toolkit that could give us an answer if fraud occurred? Well, one political scientist from the University of Michigan, Walter Mebane believes he may have developed just such a toolkit. It’s called “election forensics”. Much like machine learning algorithms, when tested in the field it does seem to perform fantastically well, but figuring out exactly how it works can be a complicated web to untangle. We give it a shot on this episode.

Om Podcasten

With all the noise created by a 24/7 news cycle, it can be hard to really grasp what's going on in politics today. We provide a fresh perspective on the biggest political stories not through opinion and anecdotes, but rigorous scholarship, massive data sets and a deep knowledge of theory. Understand the political science beyond the headlines with Harris School of Public Policy Professors William Howell, Anthony Fowler and Wioletta Dziuda. Our show is part of the University of Chicago Podcast Network.