Frameworks and methodologies for identifying, assessing, and mitigating AI risks
Essential resources in this thematic area
Voluntary framework for managing risks to individuals, organisations, and society associated with AI, developed through consensus-driven collaboration with public and private sectors.
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International standards
Peer-reviewed research
ISO, IEEE, NIST
WEF, OECD analyses
Comprehensive collection sorted by year and impact
Proposes AI risk management maturity model building on NIST AI RMF, enabling organisations to assess and advance their AI governance capabilities.
Proposes comprehensive framework for AI safety assurance combining formal verification, runtime monitoring, and safety-critical engineering practices.
Reviews AI applications in safety and reliability engineering, examining both opportunities and challenges in using AI for safety-critical systems.
Voluntary framework for managing risks to individuals, organisations, and society associated with AI, developed through consensus-driven collaboration with public and private sectors.
A systematic risk management framework released in 2023 providing guidance through four core functions: Govern, Map, Measure, and Manage.
The Responsible AI Safety and Effectiveness framework providing practical implementation pathways with over 1,100 controls mapped across 17 global standards.
Singapore's AI governance testing framework and toolkit launched in 2022, providing organizations with tools to validate AI systems against ethical principles.
A leading global nonprofit providing cutting-edge tools for responsible AI oversight and compliance through the RAISE framework.
An independent nonprofit coalition bringing together diverse communities to address AI's future through guidelines, policymaker education, and international convenings.
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