# ViSAGe: A Global-Scale Analysis of Visual Stereotypes in Text-to-Image Generation

arXiv: https://arxiv.org/abs/2401.06310 · HTML: https://mlsanity.com/paper/2401.06310/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 26 references that are in the map.
- First posted on arXiv 12 Jan 2024.

## Builds on (most cited first, 23 of 23)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale](https://mlsanity.com/paper/2211.03759/) (2022)
- [DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models](https://mlsanity.com/paper/2202.04053/) (2022)
- [Stable Bias: Analyzing Societal Representations in Diffusion Models](https://mlsanity.com/paper/2303.11408/) (2023)
- [Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchy](https://mlsanity.com/paper/1912.07726/) (2019)
- [Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction](https://mlsanity.com/paper/2210.05791/) (2022)
- [Social Biases through the Text-to-Image Generation Lens](https://mlsanity.com/paper/2304.06034/) (2023)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models](https://mlsanity.com/paper/2305.11840/) (2023)
- [FACET: Fairness in Computer Vision Evaluation Benchmark](https://mlsanity.com/paper/2309.00035/) (2023)
- [AI's Regimes of Representation: A Community-centered Study of Text-to-Image Models in South Asia](https://mlsanity.com/paper/2305.11844/) (2023)
- [Uncurated Image-Text Datasets: Shedding Light on Demographic Bias](https://mlsanity.com/paper/2304.02828/) (2023)
- [Inspecting the Geographical Representativeness of Images from Text-to-Image Models](https://mlsanity.com/paper/2305.11080/) (2023)
- ['Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion](https://mlsanity.com/paper/2310.19981/) (2023)
- [The Tail Wagging the Dog: Dataset Construction Biases of Social Bias Benchmarks](https://mlsanity.com/paper/2210.10040/) (2022)
- [Stereotypes and Smut: The (Mis)representation of Non-cisgender Identities by Text-to-Image Models](https://mlsanity.com/paper/2305.17072/) (2023)
- [Auditing Gender Presentation Differences in Text-to-Image Models](https://mlsanity.com/paper/2302.03675/) (2023)
- [Consensus and Subjectivity of Skin Tone Annotation for ML Fairness](https://mlsanity.com/paper/2305.09073/) (2023)
- [Safety and Fairness for Content Moderation in Generative Models](https://mlsanity.com/paper/2306.06135/) (2023)

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

- [VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models](https://mlsanity.com/paper/2411.13503/) (2024)
- [Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://mlsanity.com/paper/2502.12057/) (2025)
- [An image speaks a thousand words, but can everyone listen? On image transcreation for cultural relevance](https://mlsanity.com/paper/2404.01247/) (2024)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [The Case for "Thick Evaluations" of Cultural Representation in AI](https://mlsanity.com/paper/2503.19075/) (2025)
- [CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics](https://mlsanity.com/paper/2506.08835/) (2025)
- [A Large Scale Analysis of Gender Biases in Text-to-Image Generative Models](https://mlsanity.com/paper/2503.23398/) (2025)
- [Where Culture Fades: Revealing the Cultural Gap in Text-to-Image Generation](https://mlsanity.com/paper/2511.17282/) (2025)
- [Culture in Action: Evaluating Text-to-Image Models through Social Activities](https://mlsanity.com/paper/2511.05681/) (2025)
- [OASIS Uncovers: High-Quality T2I Models, Same Old Stereotypes](https://mlsanity.com/paper/2501.00962/) (2025)
- [CuRe: Cultural Gaps in the Long Tail of Text-to-Image Systems](https://mlsanity.com/paper/2506.08071/) (2025)
- [Generated Bias: Auditing Internal Bias Dynamics of Text-To-Image Generative Models](https://mlsanity.com/paper/2410.07884/) (2024)
- [Crossroads of Continents: Automated Artifact Extraction for Cultural Adaptation with Large Multimodal Models](https://mlsanity.com/paper/2407.02067/) (2024)
- [AITTI: Learning Adaptive Inclusive Token for Text-to-Image Generation](https://mlsanity.com/paper/2406.12805/) (2024)
- [An Exploration of Default Images in Text-to-Image Generation](https://mlsanity.com/paper/2505.09166/) (2025)
- [CAIRe: Cultural Attribution of Images by Retrieval-Augmented Evaluation](https://mlsanity.com/paper/2506.09109/) (2025)
- [The Digital Mirror: Gender Bias and Occupational Stereotypes in AI-Generated Images](https://mlsanity.com/paper/2510.08628/) (2025)
- [Order Is Not Layout: Order-to-Space Bias in Image Generation](https://mlsanity.com/paper/2603.03714/) (2026)
- [Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South](https://mlsanity.com/paper/2605.19190/) (2026)
- [Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling](https://mlsanity.com/paper/2607.15740/) (2026)
- [Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems](https://mlsanity.com/paper/2609.11532/) (2026)
- [Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities](https://mlsanity.com/paper/2504.06313/) (2025)
- [AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario](https://mlsanity.com/paper/2506.16898/) (2025)
- [SoS: Analysis of Surface over Semantics in Multilingual Text-To-Image Generation](https://mlsanity.com/paper/2601.16803/) (2026)
- [GLEaN: A Text-to-image Bias Detection Approach for Public Comprehension](https://mlsanity.com/paper/2604.09923/) (2026)
- [Beyond Categories of Caste: Examining Caste Bias and Morality in Text-to-Image AI Models](https://mlsanity.com/paper/2606.00039/) (2026)
- [Beyond wheelchairs and blindfolds: Investigating disability stereotypes in T2I models with INCLUDE-BENCH](https://mlsanity.com/paper/2607.08515/) (2026)

## Abstract (arXiv)

Recent studies have shown that Text-to-Image (T2I) model generations can reflect social stereotypes present in the real world. However, existing approaches for evaluating stereotypes have a noticeable lack of coverage of global identity groups and their associated stereotypes. To address this gap, we introduce the ViSAGe (Visual Stereotypes Around the Globe) dataset to enable the evaluation of known nationality-based stereotypes in T2I models, across 135 nationalities. We enrich an existing textual stereotype resource by distinguishing between stereotypical associations that are more likely to have visual depictions, such as `sombrero', from those that are less visually concrete, such as 'attractive'. We demonstrate ViSAGe's utility through a multi-faceted evaluation of T2I generations. First, we show that stereotypical attributes in ViSAGe are thrice as likely to be present in generated images of corresponding identities as compared to other attributes, and that the offensiveness of these depictions is especially higher for identities from Africa, South America, and South East Asia. Second, we assess the stereotypical pull of visual depictions of identity groups, which reveals how the 'default' representations of all identity groups in ViSAGe have a pull towards stereotypical depictions, and that this pull is even more prominent for identity groups from the Global South. CONTENT WARNING: Some examples contain offensive stereotypes.