# Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models

arXiv: https://arxiv.org/abs/2305.17072 · HTML: https://mlsanity.com/paper/2305.17072/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 40 references that are in the map.
- First posted on arXiv 26 May 2023.

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

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [The Values Encoded in Machine Learning Research](https://mlsanity.com/paper/2106.15590/) (2021)
- [Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases](https://mlsanity.com/paper/2006.03955/) (2020)
- [RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models](https://mlsanity.com/paper/2106.03521/) (2021)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021)
- [How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?](https://mlsanity.com/paper/2210.15230/) (2022)
- [Toward Gender-Inclusive Coreference Resolution](https://mlsanity.com/paper/1910.13913/) (2019)
- [Perturbation Augmentation for Fairer NLP](https://mlsanity.com/paper/2205.12586/) (2022)
- [Everyday algorithm auditing: Understanding the power of everyday users in surfacing harmful algorithmic behaviors](https://mlsanity.com/paper/2105.02980/) (2021)
- [Welcome to the Modern World of Pronouns: Identity-Inclusive Natural Language Processing beyond Gender](https://mlsanity.com/paper/2202.11923/) (2022)
- [The Stable Artist: Steering Semantics in Diffusion Latent Space](https://mlsanity.com/paper/2212.06013/) (2022)
- [Defining and Evaluating Fair Natural Language Generation](https://mlsanity.com/paper/2008.01548/) (2020)
- [How Conservative are Language Models? Adapting to the Introduction of Gender-Neutral Pronouns](https://mlsanity.com/paper/2204.10281/) (2022)
- [Black or White but never neutral: How readers perceive identity from yellow or skin-toned emoji](https://mlsanity.com/paper/2105.05887/) (2021)

## Built on by (most cited first, 27 of 27)

- [Sensitivity, Performance, Robustness: Deconstructing the Effect of Sociodemographic Prompting](https://mlsanity.com/paper/2309.07034/) (2023)
- [Survey of Bias In Text-to-Image Generation: Definition, Evaluation, and Mitigation](https://mlsanity.com/paper/2404.01030/) (2024)
- [UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images](https://mlsanity.com/paper/2405.03486/) (2024)
- [ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation](https://mlsanity.com/paper/2401.06310/) (2024)
- [Stable Diffusion Exposed: Gender Bias from Prompt to Image](https://mlsanity.com/paper/2312.03027/) (2023)
- [Harm Amplification in Text-to-Image Models](https://mlsanity.com/paper/2402.01787/) (2024)
- [A Large Scale Analysis of Gender Biases in Text-to-Image Generative Models](https://mlsanity.com/paper/2503.23398/) (2025)
- [OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes](https://mlsanity.com/paper/2501.00962/) (2025)
- [Resampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes](https://mlsanity.com/paper/2407.03623/) (2024)
- [MisgenderMender: A Community-Informed Approach to Interventions for Misgendering](https://mlsanity.com/paper/2404.14695/) (2024)
- [Un-Straightening Generative AI: How Queer Artists Surface and Challenge the Normativity of Generative AI Models](https://mlsanity.com/paper/2503.09805/) (2025)
- [Implicit Bias Injection Attacks against Text-to-Image Diffusion Models](https://mlsanity.com/paper/2504.01819/) (2025)
- [Just Because We Camp, Doesn't Mean We Should: The Ethics of Modelling Queer Voices](https://mlsanity.com/paper/2406.07504/) (2024)
- [Evaluating Model Bias Requires Characterizing its Mistakes](https://mlsanity.com/paper/2407.10633/) (2024)
- [Is What You Ask For What You Get? Investigating Concept Associations in Text-to-Image Models](https://mlsanity.com/paper/2410.04634/) (2024)
- [SoK: Towards Security and Safety of Edge AI](https://mlsanity.com/paper/2410.05349/) (2024)
- [Speciesism in Natural Language Processing Research](https://mlsanity.com/paper/2410.14194/) (2024)
- [Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Models](https://mlsanity.com/paper/2410.19812/) (2024)
- [What Is the Point of Equality in Machine Learning Fairness? Beyond Equality of Opportunity](https://mlsanity.com/paper/2506.16782/) (2025)
- [Gender Artifacts from Art History to Text-to-Image Generation](https://mlsanity.com/paper/2606.05829/) (2026)
- [A Bayesian account of pronoun and neopronoun acquisition](https://mlsanity.com/paper/2504.02973/) (2025)
- [Tell Me What You Know About Sexism: Expert-LLM Interaction Strategies and Co-Created Definitions for Zero-Shot Sexism Detection](https://mlsanity.com/paper/2504.15392/) (2025)
- [Fairness through Feedback: Addressing Algorithmic Misgendering in Automatic Gender Recognition](https://mlsanity.com/paper/2506.02017/) (2025)
- [Teaching and Critiquing Conceptualization and Operationalization in NLP](https://mlsanity.com/paper/2512.18505/) (2025)
- [SoS: Analysis of Surface over Semantics in Multilingual Text-To-Image Generation](https://mlsanity.com/paper/2601.16803/) (2026)
- [Sexualised synthetic personas encode and amplify gendered power asymmetries through voice](https://mlsanity.com/paper/2606.21366/) (2026)
- [Harmful Content Generation in Text-to-Image Models: Capabilities and Moderation Limitations](https://mlsanity.com/paper/2610.06503/) (2026)

## Abstract (arXiv)

Cutting-edge image generation has been praised for producing high-quality images, suggesting a ubiquitous future in a variety of applications. However, initial studies have pointed to the potential for harm due to predictive bias, reflecting and potentially reinforcing cultural stereotypes. In this work, we are the first to investigate how multimodal models handle diverse gender identities. Concretely, we conduct a thorough analysis in which we compare the output of three image generation models for prompts containing cisgender vs. non-cisgender identity terms. Our findings demonstrate that certain non-cisgender identities are consistently (mis)represented as less human, more stereotyped and more sexualised. We complement our experimental analysis with (a)~a survey among non-cisgender individuals and (b) a series of interviews, to establish which harms affected individuals anticipate, and how they would like to be represented. We find respondents are particularly concerned about misrepresentation, and the potential to drive harmful behaviours and beliefs. Simple heuristics to limit offensive content are widely rejected, and instead respondents call for community involvement, curated training data and the ability to customise. These improvements could pave the way for a future where change is led by the affected community, and technology is used to positively ``[portray] queerness in ways that we haven't even thought of'' rather than reproducing stale, offensive stereotypes.