Generative AI models reproduce the human biases in their training data and further amplify them through mechanisms such as mode collapse. The loss of diversity produces homogenization, which not only harms the minoritized but impoverishes everyone. We argue homogenization should be a central concern in AI safety. To meaningfully characterize homogenization in Large Language Models (LLMs), we introduce a framework that allows stakeholders to encode their context and value system. We illustrate our approach with an experiment that surfaces gender bias in an LLM (Claude 3.5 Haiku) on an open-ended story prompt. Building from queer theory, we formalize homogenization in terms of normativity. Borrowing language from feminist theory, we introduce the concept of xeno-reproduction as a class of tasks for mitigating homogenization by promoting diversity. Our work opens a collaborative line of research that seeks to understand and advance diversity in AI.

翻译：生成式人工智能模型不仅复制了训练数据中的人类偏见，还通过模式坍塌等机制进一步放大这些偏见。多样性的丧失导致了同质化，这不仅损害了少数群体，也削弱了所有人的利益。我们认为同质化应成为人工智能安全的核心关注点。为了有意义地描述大型语言模型中的同质化现象，我们引入了一个框架，允许利益相关者编码其上下文和价值体系。我们通过一项实验来阐释我们的方法，该实验揭示了大型语言模型（Claude 3.5 Haiku）在开放式故事提示中的性别偏见。基于酷儿理论，我们从规范性的角度形式化了同质化概念。借鉴女性主义理论的术语，我们引入了异质再生的概念，作为通过促进多样性来缓解同质化的一类任务。我们的工作开启了一条合作性研究路线，旨在理解和推进人工智能中的多样性。