Computer Science / Computers and Society

VEAT Quantifies Implicit Associations in Text-to-Video Generator Sora and Reveals Challenges in Bias Mitigation

February 09, 2026

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Title: VEAT Quantifies Implicit Associations in Text-to-Video Generator Sora and Reveals Challenges in Bias Mitigation
ArXiv ID: 2601.00996
Date: 2026-01-02
Authors: Yongxu Sun, Michael Saxon, Ian Yang, Anna-Maria Gueorguieva, Aylin Caliskan

📝 Abstract

Recent advancements in Text-to-Video (T2V) generators, such as Sora, have raised concerns about whether the generated content reflects societal biases. Building on prior work that quantitatively assesses associations at the word and image embedding level, we extend these methods to the domain of video generation. We introduce two novel methods: the Video Embedding Association Test (VEAT) and the Single-Category Video Embedding Association Test (SC-VEAT). We validated our approach by replicating the directionality and magnitude of associations observed in widely recognized baselines, including Implicit Association Test (IAT) scenarios and OASIS image categories. We apply our methods to measure associations related to race (African American vs. European American) and gender (male vs. female) across: (1) valence (pleasant vs. unpleasant), (2) 7 awards and 17 occupations that were stereotypically associated with a race or gender. We find that European Americans are significantly more associated with pleasantness than African Americans (d > 0.8), and women are significantly more associated with pleasantness than men (d > 0.8). Furthermore, effect sizes for race and gender...

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VEAT Quantifies Implicit Associations in Text-to-Video Generator Sora and Reveals Challenges in Bias Mitigation

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