Advances in large language models (LLMs) have driven an explosion of interest about their societal impacts. Much of the discourse around how they will impact social equity has been cautionary or negative, focusing on questions like "how might LLMs be biased and how would we mitigate those biases?" This is a vital discussion: the ways in which AI generally, and LLMs specifically, can entrench biases have been well-documented. But equally vital, and much less discussed, is the more opportunity-focused counterpoint: "what promising applications do LLMs enable that could promote equity?" If LLMs are to enable a more equitable world, it is not enough just to play defense against their biases and failure modes. We must also go on offense, applying them positively to equity-enhancing use cases to increase opportunities for underserved groups and reduce societal discrimination. There are many choices which determine the impact of AI, and a fundamental choice very early in the pipeline is the problems we choose to apply it to. If we focus only later in the pipeline -- making LLMs marginally more fair as they facilitate use cases which intrinsically entrench power -- we will miss an important opportunity to guide them to equitable impacts. Here, we highlight the emerging potential of LLMs to promote equity by presenting four newly possible, promising research directions, while keeping risks and cautionary points in clear view.

翻译：大型语言模型的发展引发了对其社会影响的极大关注。关于它们将如何影响社会公平的讨论大多持谨慎或负面态度，聚焦于诸如"大型语言模型可能如何产生偏见，我们又该如何减轻这些偏见？"这类问题。这是一个至关重要的讨论：人工智能，特别是大型语言模型，可能固化偏见的方式已有充分文献记载。但同样至关重要且讨论较少的是更具机遇导向的反面观点："大型语言模型带来了哪些能够促进公平的有前景的应用？"如果大型语言模型要实现一个更公平的世界，仅仅防御其偏见和失败模式是不够的。我们还必须主动出击，积极将其应用于促进公平的场景，为服务不足群体增加机会并减少社会歧视。有许多选择决定了人工智能的影响，而在流程早期的一个基本选择就是我们决定应用它来解决哪些问题。如果我们只关注流程后期——在大型语言模型促进本质上巩固权力的用例时，使其略微更公平——我们将错失引导其产生公平影响的重要机遇。在此，我们通过提出四个新兴且富有前景的研究方向，同时清晰呈现风险和警示要点，来强调大型语言模型促进公平的新潜力。