On the (im)possibility of sustainable artificial intelligence. Why it does not make sense to move faster when heading the wrong way

2025-03-22

AI Summary

This paper critically examines the feasibility of artificial intelligence (AI) as a tool for sustainable development, arguing that AI exacerbates Global North–South inequities across three dimensions—material resource consumption, data extraction, and sociopolitical power asymmetries—while its purported technical neutrality obscures underlying political choices. Method: Drawing on Science and Technology Studies (STS), critical data studies, and transformative sustainability science, the study develops the novel analytical framework of “AI’s triple materiality” and conducts interdisciplinary qualitative critical analysis. Contribution/Results: It advances the principles of “de-datafication” and “small is beautiful,” integrating digital degrowth, anti-extractivism, and public interest theory into AI critique. The paper demonstrates that “sustainable AI” is inherently unattainable under current paradigms and warns against techno-solutionism’s obfuscation of systemic transformation. It advocates democratically grounded, pre-emptive governance to redefine AI’s role and boundaries within sustainability transitions.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Ethics, Bias, and Fairness

Application Category

• Responsible Web: Sustainability and climate impact of web technologies
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Artificial intelligence (AI) is currently considered a sustainability"game-changer"within and outside of academia. In order to discuss sustainable AI this article draws from insights by critical data and algorithm studies, STS, transformative sustainability science, critical computer science, and public interest theory. I argue that while there are indeed many sustainability-related use cases for AI, they are likely to have more overall drawbacks than benefits. To substantiate this claim, I differentiate three 'AI materialities' of the AI supply chain: first the literal materiality (e.g. water, cobalt, lithium, energy consumption etc.), second, the informational materiality (e.g. lots of data and centralised control necessary), and third, the social materiality (e.g. exploitative data work, communities harm by waste and pollution). In all materialities, effects are especially devastating for the global south while benefiting the global north. A second strong claim regarding sustainable AI circles around so called apolitical optimisation (e.g. regarding city traffic), however the optimisation criteria (e.g. cars, bikes, emissions, commute time, health) are purely political and have to be collectively negotiated before applying AI optimisation. Hence, sustainable AI, in principle, cannot break the glass ceiling of transformation and might even distract from necessary societal change. To address that I propose to stop 'unformation gathering' and to apply the 'small is beautiful' principle. This aims to contribute to an informed academic and collective negotiation on how to (not) integrate AI into the sustainability project while avoiding to reproduce the status quo by serving hegemonic interests between useful AI use cases, techno-utopian salvation narratives, technology-centred efficiency paradigms, the exploitative and extractivist character of AI and concepts of digital degrowth.

Problem

• Examining sustainability drawbacks of AI across material, informational, and social dimensions
• Challenging apolitical AI optimization in sustainability by highlighting its inherently political nature
• Proposing alternatives to hegemonic AI integration to avoid reinforcing unsustainable status quo

Innovation

• Analyzes AI materialities in supply chain
• Critiques apolitical optimization in AI
• Proposes small-scale sustainable AI integration