AI Governance
Overview
AI governance examines who sets the rules for AI, how responsibilities are distributed, and how systems can be made accountable across their lifecycles. Approaches include risk-based regulation such as the EU AI Act, standards such as the NIST AI Risk Management Framework, and practical tools including audits, impact assessments, human oversight, incident reporting, runtime monitoring, and governance-as-code. Researchers also study public participation, rights, labor, privacy, and how governance differs across sectors and countries. A central tension is that formal policies and compliance records do not necessarily prove effective control or prevent harm, especially as AI systems become more autonomous and depend on complex supply chains.
Latest papers
386 papers28 papers in the last four weeks, down 44% on the four weeks before. 0.2% of all new papers.