可复用的语义网框架：基于证据的欧盟《人工智能法案》基本权利影响评估
A Reusable Semantic Web Framework for Evidence-Grounded Fundamental Rights Impact Assessments under the EU AI Act
- Trinity College Dublin（都柏林圣三一学院）
- ADAPT Centre（ADAPT中心）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对欧盟AI法案下基本权利影响评估证据分散的问题，提出可复用语义网框架，整合就业与公共服务领域证据，构建知识图谱，评估显示LLM辅助分类效果有限，并公开所有工件。
AI中文摘要：
欧盟《人工智能法案》（第27条）要求高风险人工智能系统的部署者在部署前进行基本权利影响评估（FRIA），然而进行可信评估所需的证据分散在不兼容的事件存储库、风险词汇和法律文本中。我们提出了一个可复用的基于语义网的框架，为两个高风险公共部门类别整合这些证据：就业和工人管理（附件III(4)）以及获得基本公共服务（附件III(5)(a)）。一个精选的150条记录语料库使用关键词、大语言模型（LLM）和混合方法沿四个轴进行标注，并序列化为一个包含1,351条RDF三元组、可通过SPARQL查询的知识图谱。五个FRIA演示场景揭示了103条记录（覆盖率68.7%）。针对69条记录黄金标准的评估显示，LLM辅助分类在就业领域仅达到κ = 0.045，这一警示性结果表明在该领域自动化公平相关证据检索存在局限。所有工件均公开发布，以支持监管机构、国家当局和中小企业的采用。
英文摘要：
The EU AI Act (Art. 27) requires deployers of high-risk AI systems to conduct Fundamental Rights Impact Assessments (FRIAs) before deployment, yet the evidence needed for credible assessments is fragmented across incompatible incident repositories, risk vocabularies, and legal texts. We present a reusable Semantic Web-based framework that consolidates this evidence for two high-risk public sector categories: employment and worker management (Annex III(4)) and access to essential public services (Annex III(5)(a)). A curated 150-record corpus is annotated along four axes using keyword, LLM, and hybrid methods and serialised as a SPARQL-queryable knowledge graph of 1,351 RDF triples. Five FRIA demonstration scenarios surface 103 records (68.7% coverage). Evaluation against a 69-record gold standard reveals that LLM-assisted classification of the employment domain achieves only $κ= 0.045$, a cautionary result for automated fairness-related evidence retrieval in this domain. All artefacts are released openly to support adoption by regulators, national authorities, and SMEs.
