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.
Abstract
As the foundation of contemporary AI systems, the semiconductor industry is increasingly shaped by critical social, environmental, and geopolitical challenges. Nonetheless, prior research has often examined these issues in isolation and within disciplinary silos, leaving a gap in understanding them through an integrative socio-technical, cross-disciplinary lens. To address this gap, we conducted a participatory futuring workshop with experts from academia, industry, and policy. The results of our analysis show that the aforementioned challenges are highly interdependent, and participants envisioned interconnected socio-technical pathways linking present frictions to normative goals. Drawing from their perspectives, we highlight three central tensions: the sovereignty--sustainability tension, where national protectionism undermines ecological survival; supply chain opacity, which obscures accountability for labor and environmental harms; and a growing knowledge divide that risks excluding smaller economies from shaping the AI future. Participants envisioned futures centered on interdependence and inclusive access, proposing measures such as a standardized emissions labeling system to translate technical data into public accountability. They also suggested frameworks for strategic interdependence to balance local resilience with global cooperation, and epistemic redistribution initiatives to lower barriers of entry and democratize access to hardware infrastructure. We argue 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.
As artificial intelligence (AI) becomes deeply embedded in human and organizational life, debates about Sustainable AI demand renewed ethical reflection. Current approaches often remain fragmented—separating AI
for
Sustainability, such as climate-mitigation applications, from the Sustainability
of
AI, including the intensive resource use and discriminatory effects of AI systems themselves—and lack a coherent moral foundation. This article addresses Sustainable AI as an integrated ethical concept, rather than focusing narrowly on either strand, and reframes it through the lens of virtue ethics, emphasizing human flourishing, moral agency, and the pursuit of the common good. Complementing this perspective with the Human-Centered AI (HCAI) paradigm, we develop an integrated governance framework that incorporates MacIntyrean categories of practices, institutions, and traditions while explicitly addressing the environmental, socio-cultural, economic, and organizational dimensions of the common good. In this framework, these four dimensions identify the substantive goods that Sustainable AI should protect and promote, while the micro (practices), meso (organizational and governance structures), and macro (cultural-historical traditions) levels specify the constructive conditions through which such goods are discerned, coordinated, and sustained over time. The framework illustrates how virtues can guide the responsible design, deployment, use, and oversight of AI systems. Our contribution is threefold: we advance Sustainable AI ethics by grounding it in virtue ethics and the common good; we extend business ethics by introducing a virtue–HCAI approach to governance; and we bridge theory and practice through a multi-level model that offers conceptual clarity and ethical orientation for long-term human flourishing.
Dulce M. Redín, M. C. Ames· Journal of Business Ethics· 0 citations
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.
Aimee van Wynsberghe, Chelsea Haramia· IEEE Energy Sustainability M...· 0 citations
This paper addresses the “AI-Green Paradox,” wherein opaque or biased algorithms can inadvertently undermine environmental, social, and governance (ESG) outcomes, and provides a highly specific, condition-aware “Monday Morning Checklist” for C-suite executives and policymakers.
Zhiyin Xiao, Guangfei Wu· Strategy & Leadership· 0 citations
It is concluded that RAI is not merely a compliance burden but the core enabling infrastructure for Industry 5.0, with its successful implementation dependent on a symbiotic fusion of policy, technology, and organizational governance.
Saurabh Chandravanshi, M. Ahmed· International Journal of Inf...· 0 citations
The Qualitative Model of Ethics (QME), a naturalistic and teleological framework grounded in a single evaluative scalar: the generative capacity of the Whole Living System (WLS), provides a unified and operational framework for AI governance at scale.
This research raises broad questions regarding social and tech ethics in the Age of AI by asking what happens if AI systems acquire full autonomy as economic actors within a late capitalist economy, specifically as workers, capitalists, and consumers. Through a careful engagement with recent Marxian social theory and the speculative literature on “full automation,” the article builds a case that the analytical lens of labor displacement needs to be enlarged to encompass the displacement of capitalists and consumers as well as workers. We propose to rethink social and tech ethics from the standpoint of a renewed attention to the constitutions of these economic actors within the capitalist system. Such a rethinking can cast new light on what it means for AI systems to be designed and deployed more ethically within the contemporary global capitalist system. It further underlines the urgency of a cross-disciplinary dialogue between social theorists and the engineers, managers, and regulators who are shaping the trajectory of such systems and, accordingly, the prospects for ethical social and economic outcomes.
J. Schulz, Laura Robinson, Katia Moles· International journal of kno...· 0 citations