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Towards a Framework for Supporting the Ethical and Regulatory Certification of AI Systems

Fabian Kovac, Sebastian Neumaier, Timea Pahi, Torsten Priebe, Rafael Rodrigues, Dimitrios Christodoulou, Maxime Cordy, Sylvain Kubler, Ali Kordia, Georgios Pitsiladis, John Soldatos, Petros Zervoudakis

TL;DR

The paper addresses the challenge of delivering regulatory-compliant, trustworthy AI within Europe’s evolving legal framework. It proposes the CERTAIN framework, which couples Semantic MLOps, ontology-driven data lineage, and RegOps workflows to integrate regulatory compliance, ethics, and transparency across the AI lifecycle. Key contributions include an EU-aligned ontology engineering approach leveraging PROV-O, ML-Schema, and RAInS; a Semantic MLOps engine for end-to-end lifecycle metadata; a Data Lineage Connector; and automated RegOps pipelines with synthetic data generation and certification templates. The work plans validation across seven pilots in biometrics, health, energy, finance, HR, and IT to demonstrate scalable, audit-ready certification pipelines and data-space–based compliance.

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.

Towards a Framework for Supporting the Ethical and Regulatory Certification of AI Systems

TL;DR

The paper addresses the challenge of delivering regulatory-compliant, trustworthy AI within Europe’s evolving legal framework. It proposes the CERTAIN framework, which couples Semantic MLOps, ontology-driven data lineage, and RegOps workflows to integrate regulatory compliance, ethics, and transparency across the AI lifecycle. Key contributions include an EU-aligned ontology engineering approach leveraging PROV-O, ML-Schema, and RAInS; a Semantic MLOps engine for end-to-end lifecycle metadata; a Data Lineage Connector; and automated RegOps pipelines with synthetic data generation and certification templates. The work plans validation across seven pilots in biometrics, health, energy, finance, HR, and IT to demonstrate scalable, audit-ready certification pipelines and data-space–based compliance.

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.

Paper Structure

This paper contains 7 sections, 1 figure.

Table of Contents

  1. Introduction and Motivation
  2. Project Overview
  3. CERTAIN Framework
  4. Semantic MLOps and Ontology Development
  5. Infrastructure and Compliance Mechanisms
  6. Current Status, Challenges, and Next Steps
  7. Acknowledgements

Figures (1)

  • Figure 1: CERTAIN's core components to systematically capture regulatory transparency and enable certification across the AI lifecycle. The architecture highlights the interplay between semantic compliance and infrastructure elements, integrating lifecycle metadata, ontological reasoning, provenance tracking, and compliance validation to ensure auditability and regulatory alignment.