评估社会应对AI风险的韧性，助力全球AI治理
Societal Capacity Assessment Framework: Measuring Resilience to Inform Advanced AI Risk Management
- 基于指标体系衡量社会脆弱性、应对力与适应力
- 将韧性分析方法移植到AI风险评估，填补上下文空白
- 适合政策制定者与组织用于提升社会准备度
先进AI系统风险评估需兼顾模型本身与部署环境。本文提出社会能力评估框架（SCAF），通过指标体系量化社会在面对AI风险时的脆弱性、应对能力和适应能力。该框架借鉴成熟的韧性分析方法，使组织能够基于国家层面的部署条件开展风险管理工作，并帮助利益相关方识别增强社会应对新兴AI能力的机遇。SCAF弥合了不同研究领域间的断层，解决AI评估中的‘上下文缺失’问题，推动更全面的风险评估与治理，以应对全球范围内先进AI系统的持续扩散。
原文摘要 · Abstract (English)
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.
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