Responsible AI vs AI Governance Explained | What’s the Difference?

What is the difference between Responsible AI and AI Governance? In this video, I explain both concepts in simple terms using definitions and perspectives from Microsoft, IBM, and IAPP. Responsible AI focuses mainly on the principles and outcomes we expect from AI, such as fairness, safety, privacy, transparency, accountability, and ethical use. AI Governance focuses on the frameworks, policies, processes, standards, roles, and controls used to make those principles work in practice. In simple terms: Responsible AI = What kind of AI do we want? Fair, safe, transparent, ethical, trustworthy, and accountable AI. AI Governance = How do we make sure this actually happens? Through policies, processes, controls, risk management, oversight, monitoring, and accountability. The video also compares how Microsoft, IBM, and IAPP describe Responsible AI and AI Governance and shows why these terms are closely related but should not be used interchangeably. This distinction is especially important for anyone working in AI governance, Responsible AI, AI risk management, AI compliance, AI audit, model risk, privacy, cybersecurity, or AI policy.