AICat: An AI Cataloguing Approach to Support the EU AI Act
Delaram Golpayegani, Harshvardhan J. Pandit, Dave Lewis
TL;DR
The paper addresses the need for a machine-readable, interoperable registry of high-risk AI systems mandated by the EU AI Act. It introduces AICat, a minimal DCAT-3 extension that leverages AIRO, DPV, and AI Use Policy to model AI systems, models, and datasets, and maps Act registration requirements to concrete metadata structures. A proof-of-concept (Proctify) demonstrates practical cataloguing, including policies and component relationships. The work aims to enhance transparency, traceability, and cross-border interoperability in AI market governance, with potential integration with MLDCAT-AP and SHACL-based normative shapes in future work.
Abstract
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.