Inspecting the Geographical Representativeness of Images from Text-to-Image Models
Open accessAbstract
Recent progress in generative models has resulted in models that produce both realistic as well as relevant images for most textual inputs. These models are being used to generate millions of images everyday, and hold the potential to drastically impact areas such as generative art, digital marketing and data augmentation. Given their outsized impact, it is important to ensure that the generated content reflects the artifacts and surroundings across the globe, rather than over-representing certain parts of the world. In this paper, we measure the geographical representativeness of common nouns (e.g., a house) generated through DALL•E 2 and Stable Diffusion models using a crowdsourced study comprising 540 participants across 27 countries. For deliberately underspecified inputs without country names, the generated images most reflect the surroundings of the United States followed by India, and the top generations rarely reflect surroundings from all other countries (average score less than 3 out of 5). Specifying the country names in the input increases the representativeness by 1.44 points on average on a 5 − point Likert scale for DALL•E 2 and 0.75 for Stable Diffusion, however, the overall scores for many countries still remain low, highlighting the need for future models to be more geographically inclusive. Lastly, we examine the feasibility of quantifying the geographical representativeness of generated images without conducting user studies.1
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The most dominant approach to gathering human annotations is to 'crowd-source' either anonymous people or community members as 'data workers', who are given simple, tokenistic tasks with little or no context to the data.
“The most dominant approach to gathering human annotations is to 'crowd-source' [7, 53, 65] either anonymous people or community members as 'data workers', who are given simple, tokenistic tasks 'that even inexperienced annotators can finish in an instance' [31, 85], with little or no context to the data.”
From Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation (2026) · cites this paper as [7]
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Non-Consensual Rhinoplasty: Misrepresentation of Female Aquiline Noses in AI-Generated Imagery
Kabát, M., & Kovalčík, J. (2025) · Communication Today · doi:10.34135/communicationtoday.2025.vol.16.no.1.7
Mukherjee, A., & Ghosh, S. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00160 · Cited by 2 · see citing works
Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South
Rastogi, C., Bhutani, M., Kahng, M., Muhammad, S. H., Razumovskaia, E., Suresh, P., Ahmad, I. S., Kalia, C., Mahomed, Y., Maji, M., et al. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812296
Bridging the Scale Gap: Augmenting Human Red-Teaming to Uncover Latent Risks in T2I Models
Quaye, J. A., Parrish, A., Rastogi, C., Kahng, M., Inel, O., Aroyo, L. M., & Reddi, V. J. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3812210
- Authors
- Basu, A., Babu, R. V., & Pruthi, D.
- Affiliations
- Indian Institute of Science Bangalore (IN)
- Venue
- 2023 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2023-10-01
- Pages
- 5113-5124
- OpenAlex
- W4390873263
- Cited by
- 21
- References
- 63
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 5 · 2023: 1
- FWCI
- 2.25
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- text-to-image models, DALL-E 2, Stable Diffusion, crowdsourced evaluation, cultural bias in AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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