# AI-generated faces influence gender stereotypes and racial homogenization

arXiv: https://arxiv.org/abs/2402.01002 · HTML: https://mlsanity.com/paper/2402.01002/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Placed in Image generation, text-to-image and editing, one of 14 directions in Diffusion models.
- Builds on 17 of its 65 references that are in the map.
- First posted on arXiv 1 Feb 2024.

## Placed in

- Diffusion models / [Image generation, text-to-image and editing](https://mlsanity.com/diffusion/image-generation-text-to-image-and-editing/)

## Builds on (most cited first, 17 of 17)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [VGGFace2: A dataset for recognising faces across pose and age](https://mlsanity.com/paper/1710.08092/) (2017)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [Fair Diffusion: Instructing Text-to-Image Generation Models on Fairness](https://mlsanity.com/paper/2302.10893/) (2023)
- [ITI-GEN: Inclusive Text-to-Image Generation](https://mlsanity.com/paper/2309.05569/) (2023)
- [T2IAT: Measuring Valence and Stereotypical Biases in Text-to-Image Generation](https://mlsanity.com/paper/2306.00905/) (2023)
- ['Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion](https://mlsanity.com/paper/2310.19981/) (2023)

## Built on by (most cited first, 14 of 14)

- [Religious Bias Landscape in Language and Text-to-Image Models: Analysis, Detection, and Debiasing Strategies](https://mlsanity.com/paper/2501.08441/) (2025)
- [FairImagen: Post-Processing for Bias Mitigation in Text-to-Image Models](https://mlsanity.com/paper/2510.21363/) (2025)
- [Exploring Vision Language Models for Facial Attribute Recognition: Emotion, Race, Gender, and Age](https://mlsanity.com/paper/2410.24148/) (2024)
- [BiasMap: Leveraging Cross-Attentions to Discover and Mitigate Hidden Social Biases in Text-to-Image Generation](https://mlsanity.com/paper/2509.13496/) (2025)
- [Reverse Personalization](https://mlsanity.com/paper/2512.22984/) (2025)
- [Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generation](https://mlsanity.com/paper/2602.00032/) (2026)
- [Visual Stereotypes of Autism Spectrum in Janus-Pro-7B, DALL-E, Stable Diffusion, SDXL, FLUX, and Midjourney](https://mlsanity.com/paper/2407.16292/) (2024)
- [A Peek Behind the Curtain: Using Step-Around Prompt Engineering to Identify Bias and Misinformation in GenAI Models](https://mlsanity.com/paper/2503.15205/) (2025)
- [RAIGen: Rare Attribute Identification in Text-to-Image Generative Models](https://mlsanity.com/paper/2602.06806/) (2026)
- [Operationalizing Fairness in Text-to-Image Models: A Survey of Bias, Fairness Audits and Mitigation Strategies](https://mlsanity.com/paper/2604.16516/) (2026)
- [Laundering AI Authority with Adversarial Examples](https://mlsanity.com/paper/2605.04261/) (2026)
- [FairGen: Preference-Aligned Diffusion for Demographically Equitable Medical Image Synthesis](https://mlsanity.com/paper/2606.14727/) (2026)
- [Learning Late, Guiding Early: Timestep-Decoupled Semantic Guidance for Fair Face Generation](https://mlsanity.com/paper/2608.25862/) (2026)
- [Small-world Networks of Agents Brainstorm AI Risks to Support Ideation](https://mlsanity.com/paper/2609.24859/) (2026)

## Abstract (arXiv)

Text-to-image generative AI models such as Stable Diffusion are used daily by millions worldwide. However, the extent to which these models exhibit racial and gender stereotypes is not yet fully understood. Here, we document significant biases in Stable Diffusion across six races, two genders, 32 professions, and eight attributes. Additionally, we examine the degree to which Stable Diffusion depicts individuals of the same race as being similar to one another. This analysis reveals significant racial homogenization, e.g., depicting nearly all Middle Eastern men as bearded, brown-skinned, and wearing traditional attire. We then propose debiasing solutions that allow users to specify the desired distributions of race and gender when generating images while minimizing racial homogenization. Finally, using a preregistered survey experiment, we find evidence that being presented with inclusive AI-generated faces reduces people's racial and gender biases, while being presented with non-inclusive ones increases such biases, regardless of whether the images are labeled as AI-generated. Taken together, our findings emphasize the need to address biases and stereotypes in text-to-image models.