AI 中文总结
该研究通过跨领域专家研讨会，指出半导体行业的社会、环境等挑战相互关联，提出需将AI伦理延伸至硬件基础设施，以实现可持续公平的半导体行业。
AI 中文摘要
作为当代AI系统的基础，半导体行业正日益受到关键的社会、环境和地缘政治挑战的塑造。然而，先前的研究往往孤立地考察这些问题，且局限于学科壁垒中，缺乏从整合性的社会技术、跨学科视角理解这些问题的研究。为填补这一空白，我们与来自学术界、产业界和政策领域的专家共同开展了一场参与式未来构想研讨会。分析结果显示，上述挑战高度相互依存，参与者构想了将当前摩擦与规范性目标相联系的相互关联的社会技术路径。基于他们的观点，我们强调三大核心张力：主权—可持续性张力，即国家保护主义破坏生态生存；供应链透明度不足，这模糊了对劳动和环境损害的问责；以及日益扩大的知识鸿沟，这可能将较小经济体排除在塑造AI未来的进程之外。参与者构想了以相互依存和包容性获取为核心的未来，提出了诸如标准化排放标签系统等措施，以将技术数据转化为公共问责依据。他们还提出了战略相互依存框架，以平衡本地韧性与全球合作，以及认知再分配举措，以降低准入门槛并使硬件基础设施的获取民主化。我们认为，AI伦理的考量必须超越模型和数据，延伸至它们所依赖的硬件基础设施，而嵌入利益相关者反思对AI物理基础设施的前瞻性治理至关重要。
英文摘要
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.
