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

CHS：小型：通过自动选票可用性评估保护选举完整性

• 批准号: 1920513
• 负责人: Michael Byrne
• 金额: $ 50万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1920513&HistoricalAwards=false
• 关键词: CHS; Small; Protecting; Election; Integrity; CHS; Small; Protecting; Election; Integrity

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.

选举中使用的选票并不总是精心设计的。投票设计中的缺陷可能会导致选民犯错，这可能会影响选举中的结果。虽然我们知道如何设计更好的选票，但人类可用性专家的管辖权太多，而不同的选票无法手动评估或对所有这些进行可用性研究。可用性评估方法将这个大问题扩展到问题。该项目将进行基础科学，以支持人类绩效计算模型的初步发展，该计算模型能够自动分析有潜在设计缺陷的选票和旗帜区域。最终目标是开发一种基于网络的工具，当地选举官员可以在实际选举中使用该工具，以便在他们的选票中检查他们的选票，从而在发生之前预防错误。通过确保每个选民的记录实际上是每个选民的意图，这有可能提高选举完整性。此外，这项研究将有助于我们理解在使用可填充表格（例如使用电子健康记录）的其他情况下出现错误的理解。该项目的目的是通过开发一种工具来解决此问题，该工具将投票作为投票时，会评估该选票是否可能导致选民错误，如果是这样，则在投票上，这些错误最有可能发生。该工具将基于使用ACT-R认知体系结构开发的计算人类绩效模型，该模型已被许多研究人员成功应用于其他可用性问题。该模型将基于对选民可用的投票完成和视觉搜索策略的探索，并通过目光传播的数据来开发。 ACT-R在场景分析方面的当前视觉功能有限，该体系结构将通过支持检测视觉群体的功能扩展，这对于了解选民如何在视觉上导航选票至关重要。初步版本已经开发并纳入了投票的初步模型中。该项目将进一步开发和评估视觉分组扩展和投票模型。这些模型将使用具有已知缺陷的选票以及新的行为数据进行验证。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准来评估的。