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，然而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。本文认为，当前沿人工智能系统部署在资源受限且相互依存的基础设施中时，这种可移植性差距可能导致以非洲为中心的重大危害路径未被充分测试。我们将重大人工智能风险定义为：源自前沿人工智能系统的实质性风险，其导致严重危害，衡量标准为造成数千人重伤或死亡，或经济损失达到一国国内生产总值的百分之五。为支持人工智能安全评估设计，我们开发了一个用于识别非洲中心化重大人工智能风险的分类体系。该分类体系将结果阈值与过程路径相连接，通过危险源、脆弱性和暴露度的交集来建模风险。我们通过放大性和突发性来区分重大风险：放大性要求前沿人工智能必须成为潜在危险的必要放大器；突发性则指危害迅速显现以至超出常规应对和治理能力的特性。随后，我们针对非洲情境提出威胁建模策略，调研了参考类别预测、结构化专家咨询、情景规划及系统理论过程分析等方法，并根据资源有限、网络连接不良、技术专业知识不足、国家能力薄弱及冲突频发等制约因素进行定制化调整。我们还探讨了人工智能错位风险，得出结论：非洲更可能通过分布偏移暴露通用失效模式，而非产生独特的错位路径。最后，我们为资源受限条件下的评估实施提供实用指导，强调采用开放可扩展的工具链、分层评估流程，并通过共享方法与研究成果来拓展评估范围。