# AICat: An AI Cataloguing Approach to Support the EU AI Act

arXiv: https://arxiv.org/abs/2501.04014 · HTML: https://mlsanity.com/paper/2501.04014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 19 Dec 2024.

## Builds on (most cited first, 4 of 4)

- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [Use case cards: a use case reporting framework inspired by the European AI Act](https://mlsanity.com/paper/2306.13701/) (2023)
- [AI Cards: Towards an Applied Framework for Machine-Readable AI and Risk Documentation Inspired by the EU AI Act](https://mlsanity.com/paper/2406.18211/) (2024)
- [Data Privacy Vocabulary (DPV) -- Version 2](https://mlsanity.com/paper/2404.13426/) (2024)

## Abstract (arXiv)

The European Union's Artificial Intelligence Act (AI Act) requires providers and deployers of high-risk AI applications to register their systems into the EU database, wherein the information should be represented and maintained in an easily-navigable and machine-readable manner. Given the uptake of open data and Semantic Web-based approaches for other EU repositories, in particular the use of the Data Catalogue vocabulary Application Profile (DCAT-AP), a similar solution for managing the EU database of high-risk AI systems is needed. This paper introduces AICat - an extension of DCAT for representing catalogues of AI systems that provides consistency, machine-readability, searchability, and interoperability in managing open metadata regarding AI systems. This open approach to cataloguing ensures transparency, traceability, and accountability in AI application markets beyond the immediate needs of high-risk AI compliance in the EU. AICat is available online at https://w3id.org/aicat under the CC-BY-4.0 license.