Recent studies have shown that Text-to-Image (T2I) model generations can reflect social stereotypes present in the real world. However, existing approaches for evaluating stereotypes have a noticeable lack of coverage of global identity groups and their associated stereotypes. To address this gap, we introduce the ViSAGe (Visual Stereotypes Around the Globe) dataset to enable the evaluation of known nationality-based stereotypes in T2I models, across 135 nationalities. We enrich an existing textual stereotype resource by distinguishing between stereotypical associations that are more likely to have visual depictions, such as `sombrero', from those that are less visually concrete, such as 'attractive'. We demonstrate ViSAGe's utility through a multi-faceted evaluation of T2I generations. First, we show that stereotypical attributes in ViSAGe are thrice as likely to be present in generated images of corresponding identities as compared to other attributes, and that the offensiveness of these depictions is especially higher for identities from Africa, South America, and South East Asia. Second, we assess the stereotypical pull of visual depictions of identity groups, which reveals how the 'default' representations of all identity groups in ViSAGe have a pull towards stereotypical depictions, and that this pull is even more prominent for identity groups from the Global South. CONTENT WARNING: Some examples contain offensive stereotypes.

翻译：近期研究表明，文本到图像（T2I）模型生成内容可能反映现实世界中的社会刻板印象。然而，现有刻板印象评估方法在全球身份群体及其关联刻板印象的覆盖范围上存在显著不足。为填补这一空白，我们提出了ViSAGe（全球视觉刻板印象）数据集，可针对135个国籍评估T2I模型中基于国籍的已知刻板印象。通过区分更可能具有视觉描绘（如"宽边帽"）与视觉具象度较低（如"有魅力"）的刻板关联，我们对现有文本刻板印象资源进行了增强。通过T2I生成内容的多维度评估，我们证明了ViSAGe的实用性。首先，与其它属性相比，ViSAGe中的刻板属性在对应身份群体的生成图像中出现频率高出三倍，且这些描绘的攻击性对非洲、南美及东南亚身份群体尤为突出。其次，我们评估了身份群体视觉描绘的刻板倾向性，这揭示了ViSAGe中所有身份群体的"默认"表征均存在向刻板描绘的倾向，且这种倾向对全球南方身份群体更为显著。内容警告：部分示例包含冒犯性刻板印象。