评估非洲中心的AI安全评估的必要性
Assessing the Case for Africa-Centric AI Safety Evaluations
AI总结：
本文探讨了非洲环境中前沿AI系统带来的严重风险，提出了一种分类方法来识别这些风险，并提出了针对非洲情境的威胁建模策略及评估指导。
摘要：
前沿AI系统正在非洲广泛采用，但大多数AI安全评估是在西方环境中设计和验证的。本文认为，可移植性差距可能导致非洲中心的风险路径未被测试，当前沿AI系统嵌入资源有限且相互依赖的基础设施中时。我们定义严重AI风险为来自前沿AI系统的物质风险，导致重大伤害，衡量为数千人严重受伤或死亡，或经济损失相当于国家GDP的5%。为了支持AI安全评估设计，我们开发了一种分类方法来识别非洲中心的严重AI风险。该分类将结果阈值与过程路径联系起来，将风险建模为危险、脆弱性和暴露的交集。我们通过放大和突然性区分严重风险，其中放大要求前沿AI是潜在危险的必要放大器，突然性捕捉到危害迅速显现足以超越普通应对和治理能力的损害。我们然后提出了针对非洲情境的威胁建模策略，调查了参考类预测、结构化专家 elicitation、情景规划和系统理论过程分析，并将其适应于资源有限、连接性差、技术专家有限、国家能力弱和冲突的约束。我们还检查了AI对齐风险，得出结论，非洲更可能通过分布偏移暴露通用失败模式，而不是生成不同的对齐路径。最后，我们提供了在资源受限情况下运行评估的实用指导，强调开放和可扩展的工具、分层评估管道以及分享方法和发现以扩大评估范围。
英文摘要：
Frontier AI systems are being adopted across Africa, yet most AI safety evaluations are designed and validated in Western environments. In this paper, we argue that the portability gap can leave Africa-centric pathways to severe harm untested when frontier AI systems are embedded in materially constrained and interdependent infrastructures. We define severe AI risks as material risks from frontier AI systems that result in critical harm, measured as the grave injury or death of thousands of people or economic loss and damage equivalent to five percent of a country's GDP. To support AI safety evaluation design, we develop a taxonomy for identifying Africa-centric severe AI risks. The taxonomy links outcome thresholds to process pathways that model risk as the intersection of hazard, vulnerability, and exposure. We distinguish severe risks by amplification and suddenness, where amplification requires that frontier AI be a necessary magnifier of latent danger and suddenness captures harms that materialise rapidly enough to overwhelm ordinary coping and governance capacity. We then propose threat modelling strategies for African contexts, surveying reference class forecasting, structured expert elicitation, scenario planning, and system theoretic process analysis, and tailoring them to constraints of limited resources, poor connectivity, limited technical expertise, weak state capacity, and conflict. We also examine AI misalignment risk, concluding that Africa is more likely to expose universal failure modes through distributional shift than to generate distinct pathways of misalignment. Finally, we offer practical guidance for running evaluations under resource constraints, emphasising open and extensible tooling, tiered evaluation pipelines, and sharing methods and findings to broaden evaluation scope.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
