Assessing the Case for Africa-Centric AI Safety Evaluations

• URL: http://arxiv.org/abs/2602.13757v1
• Date: Sat, 14 Feb 2026 13:04:52 GMT
• Title: Assessing the Case for Africa-Centric AI Safety Evaluations
• Authors: Gathoni Ireri, Cecil Abungu, Jean Cheptumo, Sienka Dounia, Mark Gitau, Stephanie Kasaon, Michael Michie, Chinasa Okolo, Jonathan Shock,
• Abstract summary: We develop a taxonomy for identifying Africa-centric severe AI risks.<n>We propose threat modelling strategies for African contexts.<n>We offer practical guidance for running evaluations under resource constraints.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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