The advent of text-to-video generation models has revolutionized content creation as it produces high-quality videos from textual prompts. However, concerns regarding inherent biases in such models have prompted scrutiny, particularly regarding gender representation. Our study investigates the presence of gender bias in OpenAI's Sora, a state-of-the-art text-to-video generation model. We uncover significant evidence of bias by analyzing the generated videos from a diverse set of gender-neutral and stereotypical prompts. The results indicate that Sora disproportionately associates specific genders with stereotypical behaviors and professions, which reflects societal prejudices embedded in its training data.

翻译：文本到视频生成模型的出现彻底改变了内容创作方式，它能够根据文本提示生成高质量视频。然而，此类模型固有的偏见问题引发了广泛关注，尤其是在性别表征方面。本研究调查了OpenAI最先进的文本到视频生成模型Sora中存在的性别偏见。通过分析一系列性别中立提示词与刻板印象提示词所生成的视频，我们发现了显著的偏见证据。结果表明，Sora模型将特定性别与刻板行为及职业进行过度关联，这反映了其训练数据中根植的社会偏见。