# A Reusable Semantic Web Framework for Evidence-Grounded Fundamental Rights Impact Assessments under the EU AI Act

arXiv: https://arxiv.org/abs/2609.39537 · HTML: https://mlsanity.com/paper/2609.39537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 30 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Ethical and social risks of harm from Language Models](https://mlsanity.com/paper/2112.04359/) (2021)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [Data Privacy Vocabulary (DPV) -- Version 2](https://mlsanity.com/paper/2404.13426/) (2024)
- [Developing an Ontology for AI Act Fundamental Rights Impact Assessments](https://mlsanity.com/paper/2501.10391/) (2024)

## Abstract (arXiv)

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 kappa = 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.