Practical Application and Limitations of AI Certification Catalogues in the Light of the AI Act

TL;DR

This work-in-progress paper evaluates the practical application and limitations of AI certification catalogues, specifically the Fraunhofer AI Assessment Catalogue, in certifying publicly available AI systems under the AI Act.

Contribution

It provides an empirical assessment of current certification approaches, highlighting their strengths, limitations, and proposing ideas to streamline AI certification processes.

Findings

Catalogue effectively structures evaluation but is cumbersome to use

Limitations found in certifying AI systems without active development teams

Emphasizes importance of complete system documentation for certification

Abstract

In this work-in-progress, we investigate the certification of AI systems, focusing on the practical application and limitations of existing certification catalogues in the light of the AI Act by attempting to certify a publicly available AI system. We aim to evaluate how well current approaches work to effectively certify an AI system, and how publicly accessible AI systems, that might not be actively maintained or initially intended for certification, can be selected and used for a sample certification process. Our methodology involves leveraging the Fraunhofer AI Assessment Catalogue as a comprehensive tool to systematically assess an AI model's compliance with certification standards. We find that while the catalogue effectively structures the evaluation process, it can also be cumbersome and time-consuming to use. We observe the limitations of an AI system that has no active development team anymore and highlighted the importance of complete system documentation. Finally, we identify some limitations of the certification catalogues used and proposed ideas on how to streamline the certification process.