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Anything that causes the vote tally to differ from the intent of the voters is a threat to election integrity. While most threats to election integrity have concerned security, there is another critical threat to election integrity: usability. When voters are unable to successfully communicate their intent due to poor ballot design, this threatens the integrity of the election, no matter what the level of security is. Traditional usability testing methods do not scale well to the tens of thousands of different ballot styles deployed across the United States in each election, so an alternative solution is necessary. This research aims to address this problem by developing the science necessary to support a tool that, when given a ballot as input, produces an assessment of whether or not that ballot is likely to lead to voter error, and if so, where on the ballot these errors are most likely to occur.
This research is based on computational human performance models developed with a well-established cognitive architecture. This architecture has been successfully applied to other usability problems by numerous researchers in the past, though never to voting. Extensions to the existing modeling system will be required in the domain of understanding visual grouping. In addition, the system will be used in a novel way. Most similar problems have been addressed by constructing a single human model that represents a single strategy. In this project, the researchers are constructing a family of models based on an exploration of the space of ballot completion and visual search strategies available to voters. Then, the stochastic model is run repeatedly at every point in the strategy space in order to discover which intersections of voter strategies and ballot designs lead to high error rates. The researchers are validating the approach using existing known bad ballots as well as with new behavioral data. The results of this research will allow the construction of a ballot analysis tool that could be used by election officials to identify potentially problematic ballots before deploying them on election day.
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EAGER: Protecting Election Integrity Via Automated Ballot Usability Evaluation