Autoregressive language models, which use deep learning to produce human-like texts, have become increasingly widespread. Such models are powering popular virtual assistants in areas like smart health, finance, and autonomous driving. While the parameters of these large language models are improving, concerns persist that these models might not work equally for all subgroups in society. Despite growing discussions of AI fairness across disciplines, there lacks systemic metrics to assess what equity means in dialogue systems and how to engage different populations in the assessment loop. Grounded in theories of deliberative democracy and science and technology studies, this paper proposes an analytical framework for unpacking the meaning of equity in human-AI dialogues. Using this framework, we conducted an auditing study to examine how GPT-3 responded to different sub-populations on crucial science and social topics: climate change and the Black Lives Matter (BLM) movement. Our corpus consists of over 20,000 rounds of dialogues between GPT-3 and 3290 individuals who vary in gender, race and ethnicity, education level, English as a first language, and opinions toward the issues. We found a substantively worse user experience with GPT-3 among the opinion and the education minority subpopulations; however, these two groups achieved the largest knowledge gain, changing attitudes toward supporting BLM and climate change efforts after the chat. We traced these user experience divides to conversational differences and found that GPT-3 used more negative expressions when it responded to the education and opinion minority groups, compared to its responses to the majority groups. We discuss the implications of our findings for a deliberative conversational AI system that centralizes diversity, equity, and inclusion.

翻译：自回归语言模型利用深度学习生成类人文本，其应用日益广泛。这类模型正驱动着智能健康、金融和自动驾驶等领域的流行虚拟助手。尽管这些大型语言模型的参数在不断优化，但人们仍担忧它们可能无法平等服务社会所有群体。尽管跨学科领域对人工智能公平性的讨论日益增多，但评估对话系统中公平性的系统性指标仍然缺失，也缺乏如何让不同群体参与评估的机制。基于协商民主理论与科技研究，本文提出一个分析框架，用以解构人机对话中公平性的内涵。运用该框架，我们开展了一项审计研究，考察GPT-3在气候变化与“黑人的命也是命”运动这两个关键科学与社会议题上，如何回应不同亚群体。我们的语料库包含GPT-3与3290名个体间的超过2万轮对话，这些个体在性别、种族与民族、教育水平、英语是否为母语以及对议题的立场上存在差异。我们发现，在意见少数群体和教育少数群体中，GPT-3的用户体验显著较差；然而，这两个群体在对话后知识获取量最大，对支持BLM运动和气候变化行动的态度发生转变。我们将用户体验差异追溯至对话特征：与对多数群体的回应相比，GPT-3在回应教育少数群体和意见少数群体时使用了更多负面表达。我们讨论这些发现对构建以多样性、公平性和包容性为核心的协商式对话人工智能系统的启示。