In an era of global sanitary, economic and ecological crisis, beliefs in the predictive power of artificial intelligence (AI) progressively penetrate the legal and political spheres, in search of new ways to anticipate and govern the future. In this context, it is critical to understand the idiosyncratic nature of the interplay between governance and algorithmic logics of prediction. This contribution discusses how the association between governance and AI makes the future knowable in the present and shapes a programmatic way of formalising, justifying and deploying action in the here and now. We focus on three principles of institutional mobilisation in the face of uncertainty and indeterminacy: precaution, pre-emption and preparedness, each of which is affected by the use of AI relying on so-called ‘real-time predictions’. Drawing from risk theory and Science and Technology Studies, we argue that the current convergence between AI and governance is shaping a new sociotechnical imaginary, promoting a distinctive conception of life and of the future in the age of the Anthropocene.

This article uses a socio-legal perspective to analyze the use of ethics guidelines as a governance tool in the development and use of artificial intelligence (AI). This has become a central policy area in several large jurisdictions, including China and Japan, as well as the EU, focused on here. Particular emphasis in this article is placed on the Ethics Guidelines for Trustworthy AI published by the EU Commission’s High-Level Expert Group on Artificial Intelligence in April 2019, as well as the White Paper on AI, published by the EU Commission in February 2020. The guidelines are reflected against partially overlapping and already-existing legislation as well as the ephemeral concept construct surrounding AI as such. The article concludes by pointing to (1) the challenges of a temporal discrepancy between technological and legal change, (2) the need for moving from principle to process in the governance of AI, and (3) the multidisciplinary needs in the study of contemporary applications of data-dependent AI.