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

arXiv: https://arxiv.org/abs/2502.10398 · HTML: https://mlsanity.com/paper/2502.10398/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 20 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers](https://mlsanity.com/paper/2406.14325/) (2024)
- [Reproducibility in Machine Learning-Driven Research](https://mlsanity.com/paper/2307.10320/) (2023)
- [Trusted Artificial Intelligence: Towards Certification of Machine Learning Applications](https://mlsanity.com/paper/2103.16910/) (2021)
- [Recommending Items in Social Tagging Systems Using Tag and Time Information](https://mlsanity.com/paper/1406.7727/) (2014)
- [Utilizing Online Social Network and Location-Based Data to Recommend Products and Categories in Online Marketplaces](https://mlsanity.com/paper/1405.1837/) (2014)
- [Artificial Intelligence in Everyday Life 2.0: Educating University Students from Different Majors](https://mlsanity.com/paper/2406.11865/) (2024)

## Abstract (arXiv)

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.