The need for fair AI is increasingly clear in the era of general-purpose
systems such as ChatGPT, Gemini, and other large language models (LLMs).
However, the increasing complexity of human-AI interaction and its social
impacts have raised questions of how fairness standards could be applied. Here,
we review the technical frameworks that machine learning researchers have used
to evaluate fairness, such as group fairness and fair representations, and find
that their application to LLMs faces inherent limitations. We show that each
framework either does not logically extend to LLMs or presents a notion of
fairness that is intractable for LLMs, primarily due to the multitudes of
populations affected, sensitive attributes, and use cases. To address these
challenges, we develop guidelines for the more realistic goal of achieving
fairness in particular use cases: the criticality of context, the
responsibility of LLM developers, and the need for stakeholder participation in
an iterative process of design and evaluation. Moreover, it may eventually be
possible and even necessary to use the general-purpose capabilities of AI
systems to address fairness challenges as a form of scalable AI-assisted
alignment.

在 ChatGPT、Gemini 和其他大型语言模型（LLMs）的普适型系统时代，迫切需要公平的人工智能。然而，人工智能与人类交互的复杂性及其社会影响引发了公平标准如何应用的问题。我们回顾了机器学习研究人员用于评估公平性的技术框架，如群体公平性和公平表示，并发现它们在应用于 LLMs 时存在固有的局限性。为了应对这些挑战，我们提出了实现特定应用案例公平性的指南：上下文的重要性，LLMs 开发者的责任以及需求利益相关者在设计和评估过程中的参与。此外，利用 AI 系统的普适能力作为可扩展的 AI 辅助调整方法可能最终可能成为可能，并且甚至是必需的。