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

TL;DR

AICat extends the Data Catalogue vocabulary to create a machine-readable, interoperable catalog for high-risk AI systems in the EU, enhancing transparency and compliance.

Contribution

The paper introduces AICat, a novel extension of DCAT-AP specifically designed for AI system cataloguing in the EU context.

Findings

Provides a standardized, machine-readable format for AI system metadata.

Enhances searchability and interoperability of AI system data.

Supports transparency and traceability in AI deployment.

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.