AI 中文总结
该研究针对生成式AI的公平挑战，指出现有表征公平策略存在局限，提出借鉴参与平等理论，从表征公平转向承认正义以解决相关问题。
AI 中文摘要
公平的AI/机器学习（ML）文献长期以来将分配公平（涉及自动化系统如何分配资源与机会）与表征公平（涉及系统如何塑造个体及社会群体被感知、理解和获得社会地位的方式）区分开来。生成式AI正在重新平衡这些规范维度。与预测系统不同，大语言模型（LLMs）及相关技术本质上具有表达性：它们的主要功能是传递意义，而非自动化特定领域的决策。表征伤害也已成为价值对齐的核心，尤其在研究AI系统应表征何种内容、哪些主体的价值观和视角的领域。现有针对社会群体表征伤害的方法常诉诸描述性准确性，但这一策略存在重要局限：对许多社会群体而言，不存在稳定或有边界的参照对象来评判表征准确性；也不清楚谁有权判定何为错误表征，且即便准确的表征也可能复制有害的社会模式。我们认为，根本问题并非单纯的错误表征，而是错误承认。我们借鉴政治理论，尤其是南希·弗雷泽（Nancy Fraser）的参与平等理论，表明从表征公平转向承认正义，能为应对生成式AI中的核心公平挑战提供更优的概念与规范工具。
英文摘要
The fair AI/ML literature has long distinguished distributive fairness, concerning how automated systems allocate resources and opportunities, from representational fairness, concerning how they shape the ways individuals and social groups are perceived, understood, and accorded social status. Generative AI is rebalancing these normative dimensions. Unlike predictive systems, large language models (LLMs) and related technologies are fundamentally expressive: their primary function is to convey meaning rather than automate domain-specific decisions. Representational harm has also become central to value alignment, especially in research on what and whose values and perspectives AI systems should represent. Existing approaches to harms in the representation of social groups often appeal to descriptive accuracy, but this strategy has important limitations. For many social groups, no stable or bounded referent exists against which representational accuracy can be judged. It is also unclear who has the authority to decide what counts as misrepresentation, while even accurate representations can reproduce harmful social patterns. The underlying problem, we argue, is therefore not simply misrepresentation but misrecognition. Drawing on political theory, especially Nancy Fraser's account of participatory parity, we show how moving from representational fairness to recognitional justice provides better conceptual and normative tools for governing central fairness challenges in generative AI.
CommentsAccepted for publication at the 2026 AAAI/ACM Conference on AI, Ethics, and Society (AIES)
