# Gender Bias in Text-to-Video Generation Models: A case study of Sora

arXiv: https://arxiv.org/abs/2501.01987 · HTML: https://mlsanity.com/paper/2501.01987/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 21 references that are in the map.
- First posted on arXiv 30 Dec 2024.

## Builds on (most cited first, 12 of 12)

- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [A Survey on Multimodal Large Language Models](https://mlsanity.com/paper/2306.13549/) (2023)
- [Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators](https://mlsanity.com/paper/2303.13439/) (2023)
- [Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models](https://mlsanity.com/paper/2402.17177/) (2024)
- [Persistent Anti-Muslim Bias in Large Language Models](https://mlsanity.com/paper/2101.05783/) (2021)
- [DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models](https://mlsanity.com/paper/2202.04053/) (2022)
- [A Survey on Video Diffusion Models](https://mlsanity.com/paper/2310.10647/) (2023)
- [GenerateCT: Text-Conditional Generation of 3D Chest CT Volumes](https://mlsanity.com/paper/2305.16037/) (2023)
- [Identifying Implicit Social Biases in Vision-Language Models](https://mlsanity.com/paper/2411.00997/) (2024)
- [Survey of Social Bias in Vision-Language Models](https://mlsanity.com/paper/2309.14381/) (2023)
- [Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models](https://mlsanity.com/paper/2407.00138/) (2024)
- [Negation Blindness in Large Language Models: Unveiling the NO Syndrome in Image Generation](https://mlsanity.com/paper/2409.00105/) (2024)

## Built on by (most cited first, 8 of 8)

- [Survey of Video Diffusion Models: Foundations, Implementations, and Applications](https://mlsanity.com/paper/2504.16081/) (2025)
- [Debiasing CLIP: Interpreting and Correcting Bias in Attention Heads](https://mlsanity.com/paper/2505.17425/) (2025)
- [Owls are wise and foxes are unfaithful: Uncovering animal stereotypes in vision-language models](https://mlsanity.com/paper/2501.12433/) (2025)
- [Surgeons Are Indian Males and Speech Therapists Are White Females: Auditing Biases in Vision-Language Models for Healthcare Professionals](https://mlsanity.com/paper/2510.06280/) (2025)
- [FAIRT2V: Training-Free Debiasing for Text-to-Video Diffusion Models](https://mlsanity.com/paper/2601.20791/) (2026)
- [A Multifaceted Analysis of Social Biases in Large Language Models](https://mlsanity.com/paper/2512.15792/) (2025)
- [When to Lock Attention: Training-Free KV Control in Video Diffusion](https://mlsanity.com/paper/2603.09657/) (2026)
- [World Simulator: Queer Erotica and the Absurdity of AI Video Models That Promise the World](https://mlsanity.com/paper/2608.07510/) (2026)

## Abstract (arXiv)

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.