AI Governance Framework
A structured set of policies, processes, and controls for managing AI development and deployment within organizations, addressing risk management, compliance, and accountability.
Only 32% of organizations using AI have formal governance frameworks, creating significant risk exposure. Key components include model risk management, bias testing, transparency requirements, data governance, and incident response. The NIST AI Risk Management Framework (AI RMF) is the most widely referenced US standard. ISO/IEC 42001 provides an international framework for AI management systems. Over 70 countries have published national AI strategies that include governance guidance. Effective governance is increasingly seen as a competitive advantage as regulation expands globally.
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Related Terms
Artificial General Intelligence (AGI)
A hypothetical form of AI that can understand, learn, and apply knowledge across any intellectual task at or above human level, rather than being specialized for specific tasks.
AI Alignment
The research field focused on ensuring AI systems behave in accordance with human values and intentions, particularly as systems become more capable.
AI Safety
The interdisciplinary field focused on preventing AI systems from causing harm, encompassing alignment, robustness, interpretability, and governance of AI technologies.
Deepfake
AI-generated synthetic media — images, video, or audio — that realistically depict events or statements that never occurred, created using deep learning techniques.
EU AI Act
The European Union's comprehensive AI regulation, which entered into force on August 1, 2024, classifying AI systems by risk level and imposing requirements from transparency disclosures to outright bans.
Hallucination
When an AI model generates plausible-sounding but factually incorrect or fabricated information, presenting it with the same confidence as accurate responses.
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