Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 77 references that are in the map. First posted on arXiv 26 Sep 2025.
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10 of 77 references.
References · 10
- 2023Sociotechnical Safety Evaluation of Generative AI Systemscited by 157
- 2023Evaluating the Social Impact of Generative AI Systems in Systems and Societycited by 92
- 2024Open Problems in Technical AI Governancecited by 49
- 2024Holistic Safety and Responsibility Evaluations of Advanced AI Modelscited by 19
- 2024Evaluating AI Evaluation: Perils and Prospectscited by 11
- 2024Societal Adaptation to Advanced AIcited by 10
- 2025Measuring AI agent autonomy: Towards a scalable approach with code inspectioncited by 10
- 2025AI Behind Closed Doors: a Primer on The Governance of Internal Deploymentcited by 8
- 2024Troubling Taxonomies in GenAI Evaluationcited by 1
- 2024Towards Responsible Governing AI Proliferationcited by 1
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Citing papers per year: 2026: 1.
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