What Is AI Governance?

AI governance operates at multiple levels: international (UN AI advisory bodies, G7 agreements), national (the EU AI Act, US executive orders on AI), industry (self-regulatory standards), and organizational (internal AI ethics boards and policies). The EU AI Act, which became the world's first comprehensive AI regulation, classifies AI systems by risk level and imposes requirements ranging from transparency obligations to outright bans on certain high-risk applications like social scoring. Key governance principles include transparency (users should know when they are interacting with AI), accountability (clear responsibility for AI decisions), fairness (preventing discriminatory outcomes), privacy (protecting personal data used in AI), and human oversight (maintaining meaningful human control). Organizations implementing AI governance typically establish AI ethics committees, conduct algorithmic impact assessments, maintain model documentation (model cards), implement bias auditing, and create processes for handling AI incidents. AI governance also encompasses data governance — ensuring training data is ethically sourced, properly licensed, and representative. As AI becomes more integrated into critical societal functions, effective governance frameworks are essential for maintaining public trust while enabling innovation.