AI-ready? How African governments are assembling policy in anticipation of data-and AI-driven techno-futures - Ndaka, Angella (LUP and African Minds, 2026)
With the current growth of artificial intelligence (AI)-driven applications in Africa, and the increasing media attention devoted to data-driven decision-making in virtually all key spaces in government as well as in society, policymakers are becoming increasingly aware that AI-driven ecosystems are inevitable. In this chapter I draw from feminist science and technology studies (STS) approaches to discuss how different governments are anticipating AI technologies, paying particular attention to the ways this anticipation relates to the framing and reframing of public policies. Of interest is the way in which policy strategies capture or mute the role of the institutions and the people who will shape AI investment, design and use, as well as overall governance. By examining the three publicly available AI policy strategies, the chapter examines how different governments on the continent are anticipating and framing AI techno-futures. The chapter responds to the question: What do the publicly available AI policy strategies reveal about how African governments are framing and anticipating AI techno-futures? It tries to compare how different policies are framed, as well as to reveal how governments see AI and big data compared to how the market sees them. The policies analysed reveal that African governments foreground technological advancement, economic growth and research, and are less focused on people and institutions whose role is important in determining how the value in the technology may be shared equitably. The chapter argues that African governments and critical AI scholars must invest in policy methodologies that counteract the tendency of large and emerging tech actors to presume an inevitable journey of converting data to monetisable knowledge and other useful products. The chapter proposes that governments and AI-critical scholars should start seeing like a market by focusing on the apparent assemblage of power, knowledge and profits, and advancing policy frameworks that require a comprehensive account of how value is extracted from data collection processes, and how this ‘value’ translates to the flourishing or disenfranchising of the populations from which data is extracted. The chapter reveals some gaps in and challenges to deliberative policy assemblage and engagement in relation to AI techno-futures in Africa. It proposes some of the ways different actors could contribute meaningfully to AI policies through deliberative policy processes.
Keywords: Artificial Intelligence(AI); AI Policy; Anticipation; People; Institutions; SDG-17: Partnerships for the goals