Hardware is an AI Ethics Problem: Expert Visions for a Sustainable and Equitable Semiconductor Industry

• URL: http://arxiv.org/abs/2608.07830v1
• Date: Sat, 08 Aug 2026 00:18:39 GMT
• Title: Hardware is an AI Ethics Problem: Expert Visions for a Sustainable and Equitable Semiconductor Industry
• Abstract summary: We conducted a participatory futuring workshop with experts from academia, industry, and policy.<n>Participants envisioned interconnected socio-technical pathways linking present frictions to normative goals.<n>We argue that AI ethics must extend beyond models and data to encompass the hardware infrastructures on which they depend.
• Score: 41.55195951929528
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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