Assessing the Case for Africa-Centric AI Safety Evaluations

TL;DR

This paper emphasizes the importance of Africa-centric AI safety evaluations, highlighting unique risks and proposing tailored threat modeling and evaluation strategies for the continent's specific infrastructural and resource constraints.

Contribution

It introduces a taxonomy for Africa-specific severe AI risks and adapts evaluation methods to resource-limited African contexts, addressing a significant gap in current AI safety research.

Findings

Africa faces unique severe AI risks due to infrastructural constraints.

Traditional safety evaluations may overlook Africa-specific pathways to harm.

Practical evaluation guidance tailored for resource-limited settings is proposed.

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.