Towards a sociotechnical ecology of artificial intelligence: power, accountability, and governance in a global context

Abstract Contemporary artificial intelligence (AI) technologies, particularly those based on foundation models and released at scale, are globally entangled and made up of a complex array of interrelated actors, practices, and transnational flows of resources. The rapid pace at which AI systems are being developed and distributed, is driving significant societal and planetary transformations. While much of the international agenda around governing AI has converged around downstream matters of safe deployment and use, deeper systemic issues—including power concentration, uneven environmental costs, or the asymmetric extraction of data and labour by technology companies—remain contested and unresolved areas of debate. In this paper we centre these systemic challenges, and locate governance entry points aimed at fostering more just futures. We conceptualise contemporary large-scale AI as a sociotechnical ecology comprised of interrelated actors, practices, and asymmetrical resource flows. Using the lens of infrastructural inversion within social studies of infrastructure, we trace the actors involved in the making of AI technologies, their interdependences, and long-term infrastructural continuities that shape them. We argue that new AI models and systems are not unprecedented but are instead built upon and shaped by pre-existing infrastructures, entrenched market relations, and socio-historical patterns. By making visible the sites of accountabilities and technical and non-technical intervention in the AI ecology, we identify four governance imperatives for sustainable and equitable AI governance: (1) Decentralising AI infrastructure, (2) Advancing environmental and epistemic justice through pluriversal AI governance, (3) Instituting cross-border data and data work governance, and (4) Enhancing international coordination, participation and solidarity.