Plain-language explanation.
AI governance refers to the policies, laws, standards, and organisational practices that guide the development and deployment of artificial intelligence in ways that are safe, ethical, and accountable. It is one of the fastest-moving fields in tech policy.
Core concepts and standard treatment.
AI governance encompasses risk-based regulation (EU AI Act tiered approach — unacceptable, high, limited, minimal risk), algorithmic accountability, bias auditing, explainability requirements (XAI), data protection integration (GDPR Article 22 on automated decisions), and voluntary frameworks such as NIST AI RMF and Singapore's AI Verify.
Deeper theory, debates and edge cases.
Advanced AI governance covers foundation model regulation (EU AI Act Article 51), AI liability directive, sector-specific rules (AI in medical devices — MDR 2017/745, AI in financial services — DORA), international AI governance coordination (G7 Hiroshima Process, GPAI), and safety evaluations for frontier AI (METR evals, UK AISI).
How it is applied in practice.
At chief AI officer and policy counsel level, AI governance professionals design enterprise AI risk management frameworks, conduct conformity assessments for high-risk AI systems, establish AI ethics boards, engage with notified bodies and market surveillance authorities, and develop AI governance strategies aligned with ISO/IEC 42001 (AI Management Systems).