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

2025-09-30

AI Summary

Facing ethical misalignment, legal non-compliance, and regulatory lag arising from the rapid deployment of AI across Europe, this paper proposes a unified governance framework integrating semantic MLOps, ontology-driven data lineage, and RegOps workflows. Grounded in computable ontology modeling, the framework enables semantic alignment and automated verification of model behavior, data provenance, and regulatory requirements across the AI lifecycle. Its key innovation lies in the first deep integration of semantic technologies into synergistic MLOps–RegOps processes—supporting formalized ethical principle representation, real-time compliance auditing, and auditable, traceable decision-making. Evaluated across multiple EU pilot deployments, the framework significantly enhances AI system transparency, verifiability, and regulatory adaptability. It delivers a scalable, dual-track pathway—combining technical infrastructure and governance mechanisms—for institutionalizing trustworthy AI.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Machine Learning: Ethics, Bias, and Fairness
• Humans and AI: AI for Accessibility

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Security and Privacy: Data transparency and provenance

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.

Problem

• Developing framework for ethical AI certification
• Integrating regulatory compliance with transparency standards
• Ensuring traceability in AI lifecycle management

Innovation

• Semantic MLOps manages structured AI lifecycle
• Ontology-driven tracking ensures data traceability
• RegOps workflows operationalize compliance requirements