AI Risk Management

AI risk management refers to coordinated activities to direct and control an organization with regard to AI-related risks. Unlike traditional software risk management, AI systems present unique challenges including:

• Data dependency: AI systems rely heavily on training data that may not represent intended use contexts
• Model opacity: Many AI systems are difficult to interpret or explain
• Emergent properties: Large-scale AI systems may exhibit unexpected behaviors
• Socio-technical nature: AI systems are influenced by societal dynamics and human behavior
• Scale and complexity: AI systems may contain billions of decision points

Effective AI risk management requires balancing multiple trustworthy AI characteristics and involves diverse stakeholders throughout the AI lifecycle. The NIST AI Risk Management Framework provides a structured approach through four core functions: GOVERN Function, MAP Function, MEASURE Function, and MANAGE Function.

Key challenges include risk measurement difficulties, determining appropriate risk tolerance, risk prioritization, and organizational integration of AI risk management with broader enterprise risk strategies.

UK Cross-Sectoral Risk Assessment

The UK's AI regulatory framework includes a central cross-sectoral risk assessment function that:

• Develops and maintains a cross-economy AI risk register
• Monitors, reviews and re-prioritizes known risks
• Identifies and prioritizes new and emerging risks
• Clarifies responsibilities for risks spanning multiple regulatory remits
• Supports coordination between regulators on cross-cutting risks

This approach recognizes that many AI risks don't fall neatly into single regulator remits and require system-wide monitoring and coordination. The framework specifically addresses 'high impact but low probability' risks including existential risks from artificial general intelligence and AI biosecurity risks.