Assessing the Case for Africa-Centric AI Safety Evaluations

Western-designed AI safety evaluations miss Africa-specific risks: The paper argues that most AI safety tests are developed and validated in Western environments, creating a "portability gap" that leaves Africa vulnerable to severe harms when frontier AI systems are deployed in contexts with materially constrained infrastructure and interdependent systems.

Africa faces unique severe risk pathways: The authors define severe AI risks as those causing critical harm (grave injury/death of thousands or economic loss equivalent to 5% of GDP) and identify that African contexts face distinct risk pathways due to limited resources, poor connectivity, weak state capacity, and conflict—factors not typically considered in standard AI safety evaluations.

Risk requires both AI amplification and rapid harm: The paper distinguishes severe African-centric risks by two key characteristics: (1) frontier AI must be a necessary magnifier of existing dangers (not just present), and (2) harms must materialize rapidly enough to overwhelm local coping and governance capacity, making prevention difficult.

Existing forecasting methods need African adaptation: The authors survey threat modeling strategies (reference class forecasting, expert elicitation, scenario planning, and system analysis) but emphasize these must be tailored to African constraints—acknowledging that standard approaches designed for resource-rich settings may not work in contexts with limited technical expertise and connectivity.

Urgent need for localized AI safety research: The paper implicitly calls for Africa-centric AI safety evaluation frameworks to be developed before frontier AI systems are widely embedded in critical African infrastructure, rather than after harms occur.