智能体化网页可访问性审计：针对WCAG的逐标准工作智能体的编写与评估
Agentic Web Accessibility Auditing: A Criterion-Specific Framework for Translating WCAG Requirements into Assessments
- University of British Columbia（不列颠哥伦比亚大学）
- Electronics and Telecommunications Research Institute（电子通信研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一个结合共享浏览器工具与逐标准工人智能体的框架，实现39项WCAG标准，在专业审计数据上召回率优于现有工具，并贡献了区分检测、证据与资源的评估方法。
AI中文摘要：
自动化可访问性评估在收集的证据和所针对的要求上各不相同。我们提出了一个框架，该框架将共享的浏览器工具与特定于标准的工人智能体相结合，实现了39项WCAG 2.1 A级和AA级标准以及一项额外的WCAG 2.2标准。我们分析了来自对学术平台进行专业审计的250条页面-标准记录中的存档预测。工人智能体恢复了0.86的正参考标签，相比之下，axe-core为0.36，未提示的视觉语言模型为0.67，但精确度较低。标准层面的结果、弃权（不执行）、开发暴露敏感性以及单独插桩的运行对这些比较进行了限定。推断出的负标签和评估配置之间的差异限制了对真实准确性和因果效应的结论。我们贡献了该框架、其特定标准的实现以及一份评估报告，该报告区分了检测、证据可用性和资源使用，从而推动在专业审计中对可检查的自动化评估进行进一步研究。
英文摘要：
Web accessibility auditing requires interpreting diverse requirements and examining interface behavior. Rule-based checks and noninteractive model assessments can miss barriers requiring contextual or interactive evidence. We present an agentic framework that assigns a vision-language agent to each accessibility requirement. Guided by tailored instructions, agents inspect webpages, operate controls, and record evidence supporting their findings. We implement the framework for 40 requirements from the Web Content Accessibility Guidelines (WCAG). To compare detection and cost, we construct a dataset of 250 page-criterion records derived from expert audits across 11 scholarly platforms. Agents recover 67 of 78 reported positive cases (86% recall), compared with 36% for axe-core, a rule-based checker, and 67% for an uncued, noninteractive vision-language model, at lower precision (56%). They recover nine of ten Keyboard and No Keyboard Trap cases missed by both baselines. Together, the framework, implementations, and dataset support automated accessibility auditing grounded in inspectable evidence.
