Text-to-Video (T2V) generators such as Sora raise concerns about whether generated content reflects societal bias. We extend embedding-association tests from words and images to video by introducing the Video Embedding Association Test (VEAT) and Single-Category VEAT (SC-VEAT). We validate these methods by reproducing the direction and magnitude of associations from widely used baselines, including Implicit Association Test (IAT) scenarios and OASIS image categories. We then quantify race (African American vs. European American) and gender (women vs. men) associations with valence (pleasant vs. unpleasant) across 17 occupations and 7 awards. Sora videos associate European Americans and women more with pleasantness (both d>0.8). Effect sizes correlate with real-world demographic distributions: percent men and White in occupations (r=0.93, r=0.83) and percent male and non-Black among award recipients (r=0.88, r=0.99). Applying explicit debiasing prompts generally reduces effect-size magnitudes, but can backfire: two Black-associated occupations (janitor, postal service) become more Black-associated after debiasing. Together, these results reveal that easily accessible T2V generators can actually amplify representational harms if not rigorously evaluated and responsibly deployed.

翻译：文本到视频（T2V）生成器（如Sora）引发了关于生成内容是否反映社会偏见的担忧。我们通过引入视频嵌入关联测试（VEAT）和单类别VEAT（SC-VEAT），将嵌入关联测试从词语和图像扩展到视频领域。我们通过复现广泛使用的基线（包括内隐联想测试（IAT）场景和OASIS图像类别）中关联的方向和强度，验证了这些方法的有效性。随后，我们量化了17种职业和7类奖项中种族（非裔美国人与欧裔美国人）和性别（女性与男性）与情感效价（愉悦与不愉悦）的关联。Sora生成的视频将欧裔美国人和女性更多地与愉悦性相关联（两者d>0.8）。效应大小与现实世界的人口分布相关：职业中男性百分比和白人百分比（r=0.93，r=0.83），以及奖项获得者中男性百分比和非黑人百分比（r=0.88，r=0.99）。应用显式去偏见提示通常能降低效应大小，但可能产生反效果：两个与黑人关联的职业（保洁员、邮政服务）在去偏见后反而与黑人关联更强。综上所述，这些结果表明，若不经严格评估和负责任部署，易于获取的T2V生成器实际上可能加剧表征性危害。