Text-to-image models are now easy to use and ubiquitous. However, prior work has found that they are prone to recapitulating harmful Western stereotypes. For example, requesting that a model generate an "African person and their house," may produce a person standing next to a straw hut. In this example, the word "African" is an explicit descriptor of the person that the prompt is seeking to depict. Here, we examine whether implicit markers, such as dialect, can also affect the portrayal of people in text-to-image outputs. We pair prompts in Mainstream American English with counterfactuals that express grammatical constructions found in dialects correlated with historically marginalized groups. We find that through minimal, syntax-only changes to prompts, we can systematically shift the skin tone and gender of people in the generated images. We conclude with a discussion of whether dialectic distribution shifts like this are harmful or are expected, possibly even desirable, model behavior.

翻译：文本到图像模型现已易于使用且无处不在。然而，先前研究发现这些模型容易复制有害的西方刻板印象。例如，要求模型生成“一位非洲人及其房屋”时，可能会生成一个站在茅草屋旁的人物。在这个例子中，“非洲人”是提示词中描述人物的显式标记。本文研究隐性标记（如方言）是否也会影响文本到图像输出中人物的呈现方式。我们将主流美国英语的提示词与表达某些与历史边缘化群体相关联的方言语法结构的反事实提示词进行配对。研究发现，仅通过对提示词进行最小的句法改动，就能系统性改变生成图像中人物的肤色和性别。最后，我们讨论了此类方言分布偏移是否属于有害或预期的（甚至可能是理想的）模型行为。