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

Fuente: arXiv
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Autores principales: Golpayegani, Delaram, Pandit, Harshvardhan J., Lewis, Dave
Formato: Preprint
Publicado: 2024
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author Golpayegani, Delaram
Pandit, Harshvardhan J.
Lewis, Dave
author_facet Golpayegani, Delaram
Pandit, Harshvardhan J.
Lewis, Dave
contents 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.
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publishDate 2024
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spellingShingle AICat: An AI Cataloguing Approach to Support the EU AI Act
Golpayegani, Delaram
Pandit, Harshvardhan J.
Lewis, Dave
Digital Libraries
Artificial Intelligence
Computers and Society
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.
title AICat: An AI Cataloguing Approach to Support the EU AI Act
topic Digital Libraries
Artificial Intelligence
Computers and Society
url https://arxiv.org/abs/2501.04014