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.

翻译：前沿人工智能系统正在非洲各地得到应用，但大多数人工智能安全评估都是在西方环境中设计和验证的。在本文中，我们认为可移植性差距可能导致前沿人工智能系统嵌入在物质受限且相互依赖的基础设施中时，以非洲为中心的严重危害路径未经测试。我们将严重人工智能风险定义为来源于前沿人工智能系统、导致关键性危害的物质风险，其衡量标准为数千人重伤或死亡，或相当于国家国内生产总值百分之五的经济损失和损害。为支持人工智能安全评估设计，我们开发了一个用于识别以非洲为中心的严重人工智能风险的分类法。该分类法将结果阈值与过程路径联系起来，将风险建模为危害、脆弱性和暴露的交集。我们通过放大性和突发性来区分严重风险，其中放大性要求前沿人工智能成为潜在危险的必要放大因素，而突发性则捕捉那些快速显现以至于超出常规应对和治理能力的危害。随后，我们针对非洲背景提出了威胁建模策略，考察了参考类预测、结构化专家启发、情景规划和系统理论过程分析，并将其定制以适应资源有限、连接性差、技术专长有限、国家能力薄弱和冲突等约束条件。我们还考察了人工智能失调风险，得出结论认为，与产生独特的失调路径相比，非洲更有可能通过分布偏移暴露普遍的失效模式。最后，我们提供了在资源限制下进行评估的实用指导，强调了开放和可扩展的工具、分层评估流程，以及共享方法和发现以扩大评估范围。