Constraining Localized Vote Tampering in the 2020 US Presidential Election
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Voter fraud in the United States is rare and the vote-counting system is robust against tampering, but there remains widespread distrust in the security of election infrastructure among the public. We consider statistical means of detecting anomalous election results that would be indicative of large-scale fraud, focusing on scenarios in which votes are modified in in a localized setting. The technique we develop, based on standard regression analysis, makes use of the fact that vote share is correlated with demographics. We apply our method to the results of the 2020 US presidential election as a proof-of-concept, resulting in uncertainties at the few-percent level. We are able to readily detect an artificial signal of such fraud in some cases, ruling out some scenarios of localized fraud and placing constraints on other scenarios.
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