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
As organizations leverage advanced digital technologies to drive sustainability – a convergence recognized as the “twin transition” – they face heterogeneous and conditional risks. This paper addresses the “AI-Green Paradox,” wherein opaque or biased algorithms can inadvertently undermine environmental, social, and governance (ESG) outcomes. It aims to provide executives with a “Responsible Intersectional Techno-Leadership” (RITL) playbook to govern these socio-technical complexities.
Rather than a general literature review, this conceptual paper employs a targeted synthesis of recent discourse across twin transition dynamics, AI ethics, and strategic management. It integrates intersectionality theory into upper echelons logic to construct an actionable, context-aware leadership methodology suitable for diverse global environments.
The analysis identifies a critical governance blind spot: the integration of digital and green strategies is not uniformly beneficial. Without intersectional oversight, AI tools can reproduce systemic historical inequalities, leading to what is formalized here as “algorithmic greenwashing.” The proposed RITL framework offers three operational levers for executives: Cognitive Audits (Strategic Awareness), Intersectional Talent Architecture (Structural Participation), and The Accountability Loop (Process Governance).
To address the operational ambiguity of the twin transition, the paper provides a highly specific, condition-aware “Monday Morning Checklist” for C-suite executives and policymakers. It offers concrete mechanisms to audit their AI supply chains and foster intersectional STEM ecosystems, ensuring that digital sustainability efforts mitigate, rather than exacerbate, corporate ESG risks across varying organizational contexts.
Moving beyond the established consensus that digitalization and sustainability are linked, this paper contributes by problematizing the twin transition. It reframes intersectional inclusion not as a compliance cost, but as a strategic asset and risk-management firewall, offering a nuanced approach to AI governance that ensures sustainable futures are equitable.
Zhiyin Xiao, Guangfei Wu· Strategy & Leadership· 0 citations
This paper provides a comprehensive analysis of the critical intersection between Responsible AI (RAI), data privacy, and the Industry 5.0 paradigm. Industry 5.0, defined by its human-centric, sustainable, and resilient pillars, introduces a fundamental paradox: its core requirement for human-AI collaboration necessitates the collection and processing of granular human data, creating direct conflicts with emerging global data privacy and AI regulations. This research utilizes a systematic integrative review methodology, analyzing peer-reviewed literature from Scopus, IEEE, Springer, and Elsevier, alongside key policy documents (e.g., EU AI Act 2024, NIST AI RMF, India’s DPDP Act 2023) and industry case studies. We argue that addressing this conflict requires a novel, integrated “Trust-by-Design Stack.” This theoretical framework combines (1) organizational governance (Privacy-by- Design), (2) Privacy-Enhancing Technologies (PETs) like Federated Learning and Differential Privacy, (3) interpretability frameworks (Explainable AI), and (4) secure network architectures (Zero-Trust). The analysis demonstrates a global schism in governance philosophies—from the EU’s rights-based model to the US’s innovation-centric approach and Asia’s state-led strategic models. Case studies of IBM, Siemens, Microsoft, Infosys, and Tesla reveal archetypal corporate strategies for navigating this fragmented landscape. The paper concludes 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