全球AI伦理框架在本地实践中常失效，因价值理解差异大
Lost in Translation: How Universal Ethical Values Fail to Translate Across Global Contexts
- 专家根据本地实际重释公平、透明等伦理概念
- 发现基础设施不平等导致技术风险认知差异
- 适合关注跨文化AI治理的研究者与政策制定者
AI伦理框架通常将公平、透明、问责等价值视为普适且可统一应用。我们调研了来自10个国家的14位专家如何在实践中理解AI，重新诠释核心价值并构想治理替代方案。研究发现，AI部署受结构性不平等影响，表现为基础设施限制、掠夺性实践及技术‘神秘化’，根本性塑造了对风险与机遇的认知。专家将价值本地化重构：隐私被理解为集体与关系性而非个体；透明被视为信任构建的责任而非技术披露；公平则体现为获取与代表性的平等而非结果均等。这些差异构成全球框架与地方实践间的‘翻译鸿沟’。最后，我们提出多元治理路径，主张重新分配知识权威，将伦理协商视为持续、情境敏感的过程，而非固定技术标准。
原文摘要 · Abstract (English)
AI ethics frameworks treat values such as fairness, transparency, and accountability as universal and uniformly operationalizable across contexts. We examined how 14 experts across 10 countries made sense of AI in practice, reinterpreted core values, and envisioned governance alternatives. We found that AI deployment is characterized by structurally unequal conditions, marked by infrastructural constraints, extractive practices, and a "mystification" of technology, which fundamentally shape perceptions of risks and opportunities. Our findings reveal that experts reinterpret values to fit local moral logics: privacy as collective and relational rather than individual; transparency as trust-building accountability rather than technical disclosure; and fairness as equity in access and representation rather than parity in outcomes. We identify these as translation gaps between encoded global frameworks and situated local practices. Finally, we propose pathways toward plural governance that redistributes epistemic authority and treats ethical negotiation as an ongoing, context-sensitive process rather than a settled technical standard.
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