Aug 2026· IEEE Energy Sustainability Magazine· Vol 2, pp. 132-139· 0 citations· 9 references
Abstract
Sustainable artificial intelligence (AI) has gained increasing prominence in academic, policy, and industry discussions, yet the concept remains underspecified and frequently deployed in ways that obscure the environmental and social harms associated with contemporary AI systems. This article argues that, for industry, sustainable AI must be understood not as a set of optional efficiency improvements or “AI for good” initiatives, but as a structural and ethical necessity. Building on the distinction between AI for sustainability and the sustainability of AI, the article situates sustainable AI within the broader evolution of AI ethics, identifying it as a hallmark of a third wave characterized by a structural turn. This perspective moves beyond artefact-level concerns such as fairness or transparency to examine AI’s embeddedness within global sociotechnical systems, including energy infrastructures, mineral extraction, data centers, supply chains, and extraplanetary technologies.
This paper argues that most responsible AI (RAI) governance frameworks do not meaningfully address AI’s environmental and social costs. Rather, they merely create the impression of managing these problems. With the example of “sustainable AI”, we show that, in its most extreme form, current RAI governance not only results in weak regulation but in a
regulatory vacuum
. Against this, we propose the concept of
critical AI governance
. With this, we reframe the governing question from “how do we make AI responsible?” to “do we need AI at all?” This approach rests on three overarching principles: (1) critically questioning dominant ideologies and economic imperatives; (2) prioritizing social and political solutions over technological fixes; (3) investing in public digital infrastructure and independent research. Together, these contributions point toward a different style of AI governance altogether.
Paul Schuetze, B. Brevini· AI & SOCIETY· 0 citations
Artificial Intelligence (AI) is reshaping scientific discovery, industrial organization, labor, and governance, with major implications for international development. AI can accelerate innovation in areas key to international development, yet the same systems can also reproduce bias, unsafe automation, environmental burdens, dependency, and unequal exposure to harms. Because the compute, data, infrastructure, and expertise needed to develop and govern advanced AI remain concentrated, the AI digital divide concerns not only who gains access, but who bears costs, who gives consent, and who shapes priorities short and long-term. This article argues that AI's developmental value, when applied to the most critical international development challenges humanity faces, still depends on the geopolitical, institutional, environmental, labor, and ethical conditions that determine how AI development and deployment benefits and risks are distributed.
Marta Koch, James Xiaolong Wang, Shreya Ravikumar et al.· MIT Science Policy Review· 1 citation
Contemporary artificial intelligence (AI) governance frameworks predominantly view AI systems as tools or discrete instruments that augment human capabilities while remaining subordinate to human intention. This paper argues that as AI systems achieve infrastructural status—embedded, ubiquitous, and foundational to cognitive activity—and hence, they demand fundamentally different regulatory logics. From this lens, we develop a systematic framework that distinguishes tool properties (visibility, optionality, task-boundedness) from infrastructure properties (invisibility, obligatoriness, epistemic generativity). We then apply this framework to evaluate six AI governance instruments adopted in North America, Europe, and Asia. Our analysis reveals a systematic regulatory gap, wherein current frameworks address what AI systems do but not what they render thinkable. To bridge this gap, we introduce the concept of “epistemic infrastructure” to capture AI’s role in constituting the conditions of possibility for thought, and propose governance mechanisms calibrated to this infrastructural function.
It is argued that considerations of AI ethics must extend beyond models and data to encompass the hardware infrastructures on which they depend, and that embedding stakeholder reflection is critical for anticipatory governance in the physical infrastructure of AI.
Naira Paola Arnez-Jordan, Chiara Ullstein, Michel Hohendanner et al.· 0 citations
Sustainable innovation is a pivotal way to address major global problems such as climate change, damage to the terrain, and social and profit inequality. In this setting, Artificial Intelligence (AI) has become a game-changing technology that can get smart, data-driven, and useful results. This paper looks into how AI can help make inventions that last in a variety of fields, such as energy, husbandry, healthcare, and community development. The study investigates how AI fosters social welfare, profitable growth, and environmental conservation, while also considering combined enterprises, including ethical business practices, energy consumption, and data sequestration. The paper also talks about the reasons behind programmes and the future of using AI in Eco-friendly fabrics. Through the operation of AI technologies, analogous to prophetic analytics and machine literacy algorithms, transformative results are being executed to address complex challenges and grease sustainable development [2]. Still, challenges analogous to ethical considerations, data insulation enterprises, vacuity issues, and the digital peak must be addressed to insure an indifferent and sustainable deployment of AI technologies for SDGs [7]. The findings indicate that, although AI possesses considerable eventuality to expedite sustainable development, its performance must be regulated by ethical and responsible practices [6].
Md Soleman Pharcy, Jiaur Rahaman, S. Barman· International Journal of Kno...· 0 citations
Ethical artificial intelligence (AI) is emerging as a widespread approach that refers to the development and use of AI technologies in a manner compatible with human values. However, the tendency of AI companies in Europe to relocate their operations to the United States is increasing debates about the applicability of this approach. One of the main reasons for this is the European Union's General Data Protection Regulation (GDPR); the costs, controls, and restrictions imposed by this regulation on data use may lead global technology firms to move outside of Europe. The high economic value-generating potential of AI creates pressure for the relaxation of these regulations, presenting a choice between ethical principles and economic interests. This study aims to provide a conceptual evaluation within the framework of the question of whether the economic gains provided by AI can outweigh human-centered ethical values. Accordingly, the research adopts a qualitative approach based on the literature and examines the current debates at a conceptual level. The assessments indicate that the European Union's desire to compete with the United States and China in the global artificial intelligence race creates a context that may make prioritizing ethical principles difficult. In this context, it is considered that the development of an ethical understanding of artificial intelligence cannot be considered independently of economic and political dynamics, and while it presents various limitations under current conditions, it offers a structure that can be improved with appropriate governance mechanisms.
Tolgay Ercan· Düzce Üniversitesi Sosyal Bi...· 0 citations
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