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

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。选票设计上的缺陷会导致选民犯错，从而影响选举结果。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选票进行可用性研究。目前还没有一种可用性评估方法能够解决如此大的问题。该项目将进行基础科学研究，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并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实的选举之前使用该工具来检查选票，从而在错误发生之前防止错误。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完整性。此外，这项研究将有助于我们了解在使用可填写表格的其他情况下如何出现错误，例如使用电子健康记录。该项目旨在通过开发一种工具来解决这一问题，该工具在提供选票作为输入时，对该选票是否可能导致选民错误进行评估，如果是这样，这些错误最有可能发生在选票上。该工具将基于ACT-R认知架构开发的计算人类绩效模型，该架构已被许多研究人员成功应用于其他可用性问题。该模型将基于对选票完成空间和选民可用的视觉搜索策略的探索，并通过眼动跟踪数据进行开发。ACT-R目前在场景分析方面的视觉功能是有限的，该架构将扩展支持视觉组检测的功能，这对于理解选民如何在视觉上浏览选票至关重要。已经开发了一个初步版本，并将其纳入初步的投票模型;该项目将进一步开发和评估视觉分组扩展和投票模型。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Missed One! How Ballot Layout and Visual Task Strategy Can Interact to Produce Voting Errors

错过了一个！

• 发表时间: 2020
• 期刊: International Conference on Cognitive Modeling
• 作者: Engels, Joshua;Wang, Xianni;Byrne, Michael D.
• 通讯作者: Byrne, Michael D.

Hierarchical Grouping of Simple Visual Scenes

简单视觉场景的层次分组

• 发表时间: 2023
• 期刊: Proceedings of the Forty-Fifth Annual Meeting of the Cognitive Science Society
• 作者: Rice, P. J.;Mao, L.;Zhu, A.;Wu, E.' &
• 通讯作者: Wu, E.' &