# "I don't see myself represented here at all": User Experiences of Stable Diffusion Outputs Containing Representational Harms across Gender Identities and Nationalities

arXiv: https://arxiv.org/abs/2408.01594 · HTML: https://mlsanity.com/paper/2408.01594/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2024.

## Built on by (most cited first, 9 of 9)

- [The Case for "Thick Evaluations" of Cultural Representation in AI](https://mlsanity.com/paper/2503.19075/) (2025)
- [More of the Same: Persistent Representational Harms Under Increased Representation](https://mlsanity.com/paper/2503.00333/) (2025)
- [VEAT Quantifies Implicit Associations in Text-to-Video Generator Sora and Reveals Challenges in Bias Mitigation](https://mlsanity.com/paper/2601.00996/) (2026)
- [What if AI systems weren't chatbots?](https://mlsanity.com/paper/2605.07896/) (2026)
- [What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity](https://mlsanity.com/paper/2506.16782/) (2025)
- [CraftGraffiti: Exploring Human Identity with Custom Graffiti Art via Facial-Preserving Diffusion Models](https://mlsanity.com/paper/2508.20640/) (2025)
- [Interrogating Design Homogenization in Web Vibe Coding](https://mlsanity.com/paper/2603.13036/) (2026)
- [Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems](https://mlsanity.com/paper/2609.11532/) (2026)
- [Identifying Harm in Personalized, Generative AI Systems Requires User-Centered Auditing at the Interaction Level](https://mlsanity.com/paper/2608.14692/) (2026)

## Abstract (arXiv)

Though research into text-to-image generators (T2Is) such as Stable Diffusion has demonstrated their amplification of societal biases and potentials to cause harm, such research has primarily relied on computational methods instead of seeking information from real users who experience harm, which is a significant knowledge gap. In this paper, we conduct the largest human subjects study of Stable Diffusion, with a combination of crowdsourced data from 133 crowdworkers and 14 semi-structured interviews across diverse countries and genders. Through a mixed-methods approach of intra-set cosine similarity hierarchies (i.e., comparing multiple Stable Diffusion outputs for the same prompt with each other to examine which result is 'closest' to the prompt) and qualitative thematic analysis, we first demonstrate a large disconnect between user expectations for Stable Diffusion outputs with those generated, evidenced by a set of Stable Diffusion renditions of `a Person' providing images far away from such expectations. We then extend this finding of general dissatisfaction into highlighting representational harms caused by Stable Diffusion upon our subjects, especially those with traditionally marginalized identities, subjecting them to incorrect and often dehumanizing stereotypes about their identities. We provide recommendations for a harm-aware approach to (re)design future versions of Stable Diffusion and other T2Is.