ContextBias：文本到圖像模型中情境轉移下偏差持續性的受控評估

摘要

文字到圖像模型會學習概念之間的關聯——就本文而言，是人们的職業（我們稱之為角色）與視覺屬性之間的關聯。這些關聯可能支撐許多被觀察到的刻板偏見形式。此領域一個關鍵的開放問題是：當專業角色的視覺表徵被置於不同的提示情境中時，這些關聯是穩定的還是會改變。我們提出 ContextBias，一個受控評估框架，以及 ContextBench，一個涵蓋 92 個角色和 1,656 個語意受控提示的基準，旨在分離情境變化對角色相關視覺表徵的影響。我們在 66,240 張生成圖像上評估四個最先進模型，發現將角色置於語意無關的情境中並不會抑制角色相關屬性；相反，跨角色屬性集中度會增加（彙總偏見指數 +0.047）。人口統計線索、代表性服裝和角色專用工具在無情境、相關情境與無關情境條件下仍然非常普遍，而且對語意提示重構具有穩健性。場景構圖與鏡頭取景則展現最大的情境敏感性。這些發現揭示了一種刻板印象持續存在的形式，而這種形式在無情境評估中幾乎不可見，凸顯了在偏見基準測試中納入受控情境變化的必要性。程式碼與資料集：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