Collaborative Research: SaTC: CORE: Medium: Bubble Aid: Assistive AI to Improve the Robustness and Security of Reading Hand-Marked Ballots

合作研究：SaTC：核心：媒介：Bubble Aid：辅助人工智能提高阅读手写选票的稳健性和安全性

• 批准号: 2154443
• 负责人: Michael Byrne
• 金额: $ 40万
• 依托单位: William Marsh Rice University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2154443&HistoricalAwards=false
• 关键词: Collaborative; Research; SaTC; CORE; Medium

In the realm of election systems that scan and process hand-marked paper ballots, the most sophisticated ones simply look at the average darkness of marks across a bubble target. This allows traditional paper ballot scanners to miss marks that are insufficiently filled in, or to miscategorize stray marks and scanner noise as filled bubbles. In addition, an inherent security problem is that these systems are not designed to identify potentially fraudulent voting cases where a singular author has filled out ballots for multiple voters. The project’s novelties, responding to these problems, is building Bubble Aid, an Artificial Intelligence (AI) system, aided by data from millions of real ballots that recognizes hand-marked bubble targets more effectively than existing systems. The project's broader significance and importance lies in improving the efficiency and security of election systems that use hand-marked ballots, with wider implications to other applications of hand-marked forms, such as standardized testing.The project is building Bubble Aid as an assistive tool which works as part of a post-election ballot auditing system, which can be used during the "canvass" period before an election is finalized. The goal is to identify the ballots where Bubble Aid disagrees with the official ballot tabulator, and present these ballots to election workers for their ultimate disambiguation. By applying state of the art techniques from the domain of image processing and deep learning, trained on a large corpus of actual ballot marks, Bubble-Aid’s goal is achieving significantly higher accuracy at hand-marked paper ballot scanning than any existing system and also detecting potential fraudulent cases of multiple ballots filled in the same hand. In designing and testing the effectiveness of Bubble Aid, the research team is conducting innovative human-participant experiments for a variety of tasks related to this work. This includes requesting participants to mark test ballots, deliberately injecting noise into them, or filling multiple ballots to emulate the fraudulent scenario, in order to gain additional training 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.

在扫描和处理手工标记的纸质选票的选举系统领域中，最复杂的选票只是看着气泡目标上的标记的平均黑暗。这使传统的纸质投票扫描仪会错过不足以填充的标记，或者将流浪痕迹和扫描仪噪音误导为填充的气泡。此外，一个固有的安全问题是，这些系统并非旨在确定潜在的欺诈性投票案例，在这些案例中，单数作者填写了为多个选民的选票。该项目的新颖性是对这些问题的回应，是建立泡沫援助，一种人工智能（AI）系统，在数百万个真实投票中的数据中，比现有系统更有效地认识到手动标记的泡泡目标。该项目的更广泛的意义和重要性在于提高使用手工标记的选票的选举系统的效率和安全性，对其他手动标记形式的其他应用具有更广泛的影响。目的是确定泡沫援助与官方投票表不同意的选票，并向选举工人提出这些选票，以实现他们的最终歧义。通过从图像处理和深度学习的范围内运用对实际投票标记的训练的域名，Bubble-AID的目标是在手工标记的纸质投票扫描中获得明显更高的准确性，并且还可以检测到同一手中填充的多个选票的潜在欺诈案例。在设计和测试气泡援助的有效性时，研究团队正在为与这项工作有关的各种任务进行创新的人参与实验。这包括要求参与者标记测试选票，故意向其注入噪音，或填写多次选票以模仿狂热的场景，以获取其他培训数据。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准来通过评估来通过评估来获得的支持。