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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2403.16808 |
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| _version_ | 1866913426323800064 |
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| author | Kelly, J. Zafar, S. Heidemann, L. Zacchi, J. Espinoza, D. Mata, N. |
| author_facet | Kelly, J. Zafar, S. Heidemann, L. Zacchi, J. Espinoza, D. Mata, N. |
| contents | In December 2023, the European Parliament provisionally agreed on the EU AI Act. This unprecedented regulatory framework for AI systems lays out guidelines to ensure the safety, legality, and trustworthiness of AI products. This paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems by leveraging product quality models. We first propose an extended product quality model for AI systems, incorporating attributes relevant to the Act not covered by current quality models. We map the Act requirements to relevant quality attributes with the goal of refining them into measurable characteristics. We then propose a contract-based approach to derive technical requirements at the stakeholder level. This facilitates the development and assessment of AI systems that not only adhere to established quality standards, but also comply with the regulatory requirements outlined in the Act for high-risk (including safety-critical) AI systems. We demonstrate the applicability of this methodology on an exemplary automotive supply chain use case, where several stakeholders interact to achieve EU AI Act compliance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_16808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Navigating the EU AI Act: A Methodological Approach to Compliance for Safety-critical Products Kelly, J. Zafar, S. Heidemann, L. Zacchi, J. Espinoza, D. Mata, N. Artificial Intelligence In December 2023, the European Parliament provisionally agreed on the EU AI Act. This unprecedented regulatory framework for AI systems lays out guidelines to ensure the safety, legality, and trustworthiness of AI products. This paper presents a methodology for interpreting the EU AI Act requirements for high-risk AI systems by leveraging product quality models. We first propose an extended product quality model for AI systems, incorporating attributes relevant to the Act not covered by current quality models. We map the Act requirements to relevant quality attributes with the goal of refining them into measurable characteristics. We then propose a contract-based approach to derive technical requirements at the stakeholder level. This facilitates the development and assessment of AI systems that not only adhere to established quality standards, but also comply with the regulatory requirements outlined in the Act for high-risk (including safety-critical) AI systems. We demonstrate the applicability of this methodology on an exemplary automotive supply chain use case, where several stakeholders interact to achieve EU AI Act compliance. |
| title | Navigating the EU AI Act: A Methodological Approach to Compliance for Safety-critical Products |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2403.16808 |