ContextBias：上下文偏移下文生图模型偏见持续性的受控评估

摘要

文本到图像模型学习概念之间的关联——在本文中，即人们的职业（我们称之为角色）——与视觉属性之间的关联。这些关联可能支撑许多观察到的刻板偏见形式。该领域的一个关键开放问题是：当专业角色中人物的视觉表征被置于不同的提示语境中时，这些关联是稳定的还是会发生变化。我们引入了ContextBias，一个受控评估框架，以及ContextBench，一个涵盖92个角色和1,656个语义受控提示的基准测试，旨在隔离上下文变化对角色相关视觉表征的影响。通过在66,240张生成图像上评估四种最先进的模型，我们发现将角色置于语义无关的语境中并不会抑制角色相关的属性；相反，跨角色属性集中度有所增加（合并BI +0.047）。人口统计学线索、特征性服装和角色特定工具在无上下文、相关和不相关条件下仍然高度普遍存在，并且对语义提示重构具有稳健性。场景构图和镜头取景表现出最大的上下文敏感性。这些发现揭示了一种刻板持续性形式，这种形式在无上下文评估中基本不可见，突显了在偏见基准测试中引入受控上下文变化的必要性。代码和数据集：https://huggingface.co/datasets/shaghayegh/ContextBias , https://github.com/Sina-Emami/ContextBias

English

Text-to-image models learn associations between concepts - in the case of this paper, people's professions, which we refer to as roles - and visual attributes. These associations can underpin many observed forms of stereotypical bias. A key open question in this area is whether these associations are stable or change when visual representations of people in professional roles are placed in different prompted contexts. We introduce ContextBias, a controlled evaluation framework, and ContextBench, a benchmark spanning 92 roles and 1,656 semantically controlled prompts, designed to isolate the effect of contextual variation on role-linked visual representations. Evaluating four state-of-the-art models on 66,240 generated images, we find that placing a role in a semantically unrelated context does not suppress role-linked attributes; instead, cross-role attribute concentration increases (pooled BI +0.047). Demographic cues, characteristic garments, and role-specific tools remain highly prevalent across context-free, related, and unrelated conditions, and are robust to semantic prompt reformulation. Scene composition and camera framing show the greatest context-sensitivity. These findings reveal a form of stereotypical persistence that remains largely invisible to context-free evaluations, highlighting the need for controlled contextual variation in bias benchmarking. Code and dataset: https://huggingface.co/datasets/shaghayegh/ContextBias , https://github.com/Sina-Emami/ContextBias