AI – Ready? How African governments are assembling policy in anticipation for data and AI driven techno-futures.

With the current growth of artificial Intelligence (AI)-driven applications in Africa, and the increasing media attention on data-driven decision-making in virtually all key spaces in government as well as the society, policy makers are becoming increasingly aware that AI-driven ecosystems are inevitable. In this chapter I draw from feminist STS approaches to discuss how different governments are anticipating AI technologies, paying particular attention in the ways this anticipation relates to framing and reframing of public policies. Of interest is the way the policy strategies capture or mute the role of the institutions and the people that will shape AI investment, design and use, and overall governance. By examining the three publicly available AI policy strategies, the chapter examines how different governments in 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 what they reveal about how the governments see AI and big data compared to how the market sees it. The policies analysed reveal that African governments foregrounds technological advancement, economic growth, and research, and less focused on people and institutions whose role is important in determining how the value in the technology is shared equitably. The chapter argues that African governments and critical AI scholars need to invest in policy methodologies that counteract the tendency of large and emerging tech actors from presuming an inevitable journey of converting data to monetizable 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 and challenges to deliberative policy assemblage and engagement in relation to AI techno-futures in Africa. It proposes some of the ways different actors can contribute meaningfully to AI policies through deliberative policy processes.