Linked.Archi AI Ethics & Governance Metamodel Definition

Extension ontology for AI ethics and governance. Provides element types, properties, and reference data for managing AI system risk classification, conformity assessment, bias assessment, explainability documentation, human oversight plans, and governance policies. Builds on the ML-Enabled Systems extension (mlsys:) by adding the governance layer that connects ML components to regulatory frameworks (EU AI Act), ethical principles (OECD AI Principles), and organizational AI governance policies. Motivated by Gartner 2025 Leadership Vision identifying AI ethics and governance as a critical gap in EA teams, and by the EU AI Act (Regulation 2024/1689) requiring formal risk classification and conformity assessment for high-risk AI systems.

SHACL shapes for validating AI governance models. Enforces governance rules: every AI system must have a risk classification, high-risk systems must have conformity assessments and human oversight plans, and all AI systems must have explainability documentation.

Classification of AI governance concepts by risk level, principle, lifecycle phase, and assessment type.

Reference data for AI governance — EU AI Act risk levels, OECD AI Principles, human oversight modes, and assessment statuses.