Case Studies of AI Policy Development in Africa

TL;DR

The paper investigates why global AI readiness measures fail to capture Africa's nuanced progress by integrating four country case studies with a critical review of existing frameworks. It combines a systematic analysis of AI policy indicators with qualitative country-level insights to argue for Africa-specific evaluation components. The authors propose a tailored African AI Policy Evaluation Framework and outline future indicators that emphasize data governance, enabling environments, and regional collaboration, aiming to produce more accurate, actionable assessments for policymakers. The work has practical significance by guiding targeted reforms and regional cooperation to accelerate AI readiness in Africa, rather than relying on one-size-fits-all global indices.

Abstract

Artificial Intelligence (AI) requires new ways of evaluating national technology use and strategy for African nations. We conduct a survey of existing 'readiness' assessments both for general digital adoption and for AI policy in particular. We conclude that existing global readiness assessments do not fully capture African states' progress in AI readiness and lay the groundwork for how assessments can be better used for the African context. We consider the extent to which these indicators map to the African context and what these indicators miss in capturing African states' on-the-ground work in meeting AI capability. Through case studies of four African nations of diverse geographic and economic dimensions, we identify nuances missed by global assessments and offer high-level policy considerations for how states can best improve their AI readiness standards and prepare their societies to capture the benefits of AI.