Towards a Framework for Supporting the Ethical and Regulatory Certification of AI Systems
Abstract
Artificial Intelligence has rapidly become a cornerstone technology, significantly influencing Europe's societal and economic landscapes. However, the proliferation of AI also raises critical ethical, legal, and regulatory challenges. The CERTAIN (Certification for Ethical and Regulatory Transparency in Artificial Intelligence) project addresses these issues by developing a comprehensive framework that integrates regulatory compliance, ethical standards, and transparency into AI systems. In this position paper, we outline the methodological steps for building the core components of this framework. Specifically, we present: (i) semantic Machine Learning Operations (MLOps) for structured AI lifecycle management, (ii) ontology-driven data lineage tracking to ensure traceability and accountability, and (iii) regulatory operations (RegOps) workflows to operationalize compliance requirements. By implementing and validating its solutions across diverse pilots, CERTAIN aims to advance regulatory compliance and to promote responsible AI innovation aligned with European standards.
Cite
@article{arxiv.2510.00084,
title = {Towards a Framework for Supporting the Ethical and Regulatory Certification of AI Systems},
author = {Fabian Kovac and Sebastian Neumaier and Timea Pahi and Torsten Priebe and Rafael Rodrigues and Dimitrios Christodoulou and Maxime Cordy and Sylvain Kubler and Ali Kordia and Georgios Pitsiladis and John Soldatos and Petros Zervoudakis},
journal= {arXiv preprint arXiv:2510.00084},
year = {2025}
}
Comments
Accepted for publication in the proceedings of the Workshop on AI Certification, Fairness and Regulations, co-located with the Austrian Symposium on AI and Vision (AIRoV 2025)