Beyond AI as an environmental pharmakon: Principles for reopening the problem-space of machine learning's carbon footprint

In this paper, we critique a pervasive discourse about the environmental implications of artificial intelligence as witnessed in news media, public policy analysis and computer science literature. In this discourse, AI is seen through a paradoxical lens: as essential to reducing the damaging effects of the climate crisis and, at the same time, a looming threat to both the climate and broader ecological crises. This seemingly contradictory framing of AI as both ‘remedy’ and ‘poison’ resonates with the concept of pharmakon, a heuristic device used extensively in the philosophy of technology. In this paper we show how the policy discourses of leading actors such as the OECD, Green Software Foundation and Microsoft's data scientists resolve the pharmacological nature of AI's environmental impact by narrowing the scope of its toxic properties and hence the solutions required to enable the technology's continued use and expansion. We argue that these discourses are reducing and oversimplifying the problem at stake to a simple proposition: we need more AI for climate tech applications but less energy thirsty AI. We show how this framing of the problem arose from a particular recent political history of the ‘techlash’, which in turn prompted considerable efforts to quantify AI's carbon footprint. We suggest a different problematisation inspired by science and technology studies scholar Andrew Barry's methodological approach, one that can re-open the problem-space of AI's environmental impact. This approach is sketched through four methodological starting points: unpacking the material entanglements between AI and ecologies; being sensible to geohistory – the specific locally situated nature of data centres and energy grids sustaining AI training, tuning and deployment; envisioning the multiplicity of solutions to the climate crisis (beyond carbon accounting of the AI footprint); and finally, rereading AI (by acknowledging the heterogeneity of actors and interests along AI supply chains).