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AI governance

AI governance is the set of policies, processes, and oversight mechanisms used to manage how artificial intelligence is designed, developed, deployed, and monitored. Its goal is to ensure AI systems are safe, lawful, ethical, and aligned with organizational and societal expectations. Key areas often include risk manag

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  1. AI governance (en-US)

    AI governance is the set of policies, processes, and oversight mechanisms used to manage how artificial intelligence is designed, developed, deployed, and monitored. Its goal is to ensure AI systems are safe, lawful, ethical, and aligned with organizational and societal expectations. Key areas often include risk management (identifying and mitigating harms), accountability (clear ownership for decisions and outcomes), transparency (documenting how models work and how decisions are made), and human oversight (ensuring people can review or intervene when needed). Governance also covers data stewardship (privacy, security, and data quality), model evaluation (testing for performance and bias), and compliance with relevant laws and standards. Effective governance typically involves lifecycle controls—requirements at planning, safeguards during development, monitoring after deployment, and incident response. Many frameworks also emphasize stakeholder engagement, auditability, and continuous improvement as models and environments change.

  2. Common components

    Typical components include: (1) an AI policy and scope; (2) a risk classification or impact assessment; (3) technical and procedural controls (e.g., documentation, testing, access controls); (4) review and approval workflows; (5) monitoring and reporting; and (6) training for staff and escalation paths for issues.

FAQ

What is the difference between AI governance and AI ethics?

AI ethics focuses on principles (e.g., fairness, transparency), while AI governance turns principles into enforceable rules, processes, and oversight.

Who is responsible for AI governance in an organization?

Usually a cross-functional group (e.g., legal, security, risk, product, engineering, and compliance) with defined owners for approvals and accountability.

How is AI governance enforced?

Through documented requirements, review gates, audits/assessments, monitoring metrics, and corrective actions when risks or incidents are identified.

Client endpoint

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