Assessing the Case for Africa-Centric AI Safety Evaluations

2026-02-14

AI Summary

This study addresses the critical gap in AI safety assessments, which are predominantly grounded in Western contexts and often fail to capture severe, Africa-specific risks—particularly under conditions of resource scarcity, weak infrastructure, and limited governance capacity. The work presents the first systematic definition of high-severity AI risks in the African context, introducing “amplification” and “emergence” as core criteria for risk identification, and linking general alignment failure modes to region-specific distributional shifts. By integrating reference class forecasting, structured expert elicitation, scenario planning, and systems-theoretic process analysis, the authors develop a tiered evaluation pipeline and an open-source toolchain tailored for low-resource settings. The resulting framework, along with its accompanying guidelines and operational protocols, significantly enhances Africa’s capacity to identify and mitigate high-risk AI harms within its unique technological and institutional constraints.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: AI for Accessibility
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Security and Privacy: Security and privacy of machine learning and AI applications

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.

Problem

• AI safety
• Africa-centric risks
• frontier AI
• severe harm
• distributional shift

Innovation

• Africa-centric AI safety
• severe AI risks
• risk taxonomy
• threat modeling
• resource-constrained evaluation