CHS: Small: Protecting Election Integrity Via Automated Ballot Usability Evaluation

选举中使用的选票并不总是精心设计的。选票设计中的缺陷可能会导致选民出错，这可能会影响选举的结果。虽然我们知道如何设计更好的选票，但有太多的司法管辖区和太多不同的选票，人类可用性专家无法手动评估或对所有这些进行可用性研究。一种适用于如此大的问题的可用性评估方法还不存在。该项目将进行基础科学工作，以支持人类表现计算模型的初步开发，该模型能够自动分析选票和标记存在潜在设计缺陷的区域。最终目标是开发一种基于网络的工具，地方选举官员可以在选票用于真正的选举之前用它来检查选票，从而在错误发生之前防止错误发生。这有可能通过确保每张选票上记录的内容实际上是每个选民的意图来提高选举的廉洁性。此外，这项研究将有助于我们理解在使用可填写表格的其他情况下，例如使用电子健康记录时，错误是如何出现的。该项目旨在通过开发一种工具来解决这一问题，当提供一张选票作为投入时，该工具可以评估该选票是否可能导致选民错误，如果可能，这些错误最有可能发生在选票上的哪些地方。该工具将基于使用ACT-R认知架构开发的计算人类绩效模型，该模型已被众多研究人员成功地应用于其他可用性问题。该模型将根据选民可利用的选票完成空间和视觉搜索战略的探索以及眼球跟踪数据进行开发。ACT-R目前在场景分析方面的视觉能力是有限的，该架构将扩展为支持视觉群体检测的能力，这对于了解选民如何视觉导航投票至关重要。已经开发了一个初步版本，并将其纳入投票的初步模式；该项目将进一步开发和评估视觉分组扩展和投票模式。这些模型将使用带有已知缺陷的选票以及新的行为数据进行验证。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Ballots used in elections are not always well-designed. Flaws in ballot design can lead voters to make errors, and this can affect the outcome in a election. While we know how to design better ballots, there are too many jurisdictions and too many different ballots for human usability experts to manually evaluate or conduct usability studies on all of them. A usability evaluation method that scales to a problem this large does not yet exist. This project will do the basic science to support the initial development of a computational model of human performance that is able to automatically analyze a ballot and flag areas where there are potential design flaws. The ultimate goal is to develop a web-based tool that local election officials could use to check their ballots before they are used in a real election, thus preventing errors before they happen. This has the potential to improve election integrity by ensuring that what is recorded on each ballot is actually what each voter intended. In addition, this research will contribute to our understanding of how errors emerge in other contexts where fillable forms are used, such as use of electronic health records. The project aims to address this problem by developing 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 tool will be based on computational human performance models developed with the ACT-R cognitive architecture, which has been successfully applied to other usability problems by numerous researchers. The model will be developed based on an exploration of the space of ballot completion and visual search strategies available to voters and informed by eye-tracking data. ACT-R's current visual capabilities in terms of scene analysis are limited, and the architecture will be extended with capabilities to support detection of visual groups, which are critical in understanding how voters visually navigate a ballot. A preliminary version has already been developed and incorporated into preliminary models of voting; this project will further develop and evaluate both the visual grouping extension and the voting models. The models will be validated using ballots with known flaws as well as with new behavioral data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.