Big Data, Artificial Intelligence (AI) and Machine Learning (ML) are reshaping how governance is conceived, negotiated and deployed. In Africa, expectations are mounting around the potential of AI/ML systems to make polities more efficient, accountable and resilient vis-à-vis developmental challenges and anticipated environmental shocks. Although currently at an experimental stage, most AI-driven initiatives are catalysing the attention of national governments, institutional donors and corporate actors. But while national agendas and policy strategies are being laid out, there is still a lack of clarity on how to prevent or minimise the negative externalities that a growing literature on AI/ML is highlighting. Scholarly concerns span from function creep, or the repurposing of citizen technologies and data for commercial or security aims, to the risk of reproducing pre-existing biases into predictive models, to an overreliance of the public sector on corporate, and often foreign, players. In this research theme, we use a comparative perspective to explore the technopolitics of AI in Africa, focusing on the mutual shaping of politics and digital technologies in Kenya, South Africa, Rwanda and Ethiopia. We look in particular at how policymakers, the private sector and international donors influence the trajectories of AI agendas and the role of local normative and governance frameworks in shaping their implementation.

AI Governance

AI Bias