Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management
This work addresses the need for holistic risk assessment in AI deployment contexts, bridging gaps in evaluation for stakeholders like organizations and policymakers, though it is incremental as it adapts existing resilience methodologies.
The authors tackled the problem of evaluating societal resilience to AI-related risks by introducing the Societal Capacity Assessment Framework (SCAF), which measures vulnerability, coping capacity, and adaptive capacity to inform risk management and governance.
Risk assessments for advanced AI systems require evaluating both the models themselves and their deployment contexts. We introduce the Societal Capacity Assessment Framework (SCAF), an indicators-based approach to measuring a society's vulnerability, coping capacity, and adaptive capacity in response to AI-related risks. SCAF adapts established resilience analysis methodologies to AI, enabling organisations to ground risk management in insights about country-level deployment conditions. It can also support stakeholders in identifying opportunities to strengthen societal preparedness for emerging AI capabilities. By bridging disparate literatures and the "context gap" in AI evaluation, SCAF promotes more holistic risk assessment and governance as advanced AI systems proliferate globally.