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AI Certification: An Accreditation Perspective (Practitioner Track)

Authors Susanne Kuch, Raoul Kirmes

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Artificial intelligence
  • General and reference → Computing standards, RFCs and guidelines
Keywords
  • certification
  • conformity assessment
  • market entry
  • accreditation
  • artificial intelligence
  • standard

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Abstract

AI regulations worldwide set new requirements for AI systems, leading to thriving efforts to develop testing tools, metrics and procedures to prove their fulfillment. While such tools are still under research and development, this paper argues that the procedures to perform conformity assessment, especially certification, are largely in place. It provides an overview of how AI product certifications work based on international standards (ISO/IEC 17000 series) and what elements are missing from an accreditation perspective. The goal of this paper is to establish a common understanding of how conformity assessment in general and certification in particular work regarding AI systems.

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Susanne Kuch and Raoul Kirmes. AI Certification: An Accreditation Perspective (Practitioner Track). In Symposium on Scaling AI Assessments (SAIA 2024). Open Access Series in Informatics (OASIcs), Volume 126, pp. 14:1-14:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.SAIA.2024.14

Author Details

Susanne Kuch
  • Deutsche Akkreditierungsstelle (DAkkS), Stabsbereich Akkreditierungsgovernance, Forschung und Innovation, Berlin, Germany
Raoul Kirmes
  • Deutsche Akkreditierungsstelle (DAkkS), Stabsbereich Akkreditierungsgovernance, Forschung und Innovation, Berlin, Germany

Acknowledgements

Special thanks are due to Mattis Jacobs for his support with this paper, and to Dominic Deuber and Svenja Reisinger for their feedback.

References

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