Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities

[2504.06313] Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2504.06313 (cs) [Submitted on 8 Apr 2025 ( v1 ), last revised 11 Apr 2026 (this version, v5)] Title: Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities Authors: Abdulkareem Alsudais View a PDF of the paper titled Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities, by Abdulkareem Alsudais View PDF Abstract: This paper investigates how popular text-to-image (T2I) models, DALL-E 3 and Gemini 3 Pro Preview, depict people from 206 nationalities when prompted to generate images of individuals engaging in common everyday activities. Five scenarios were developed, and 2,060 images were generated using input prompts that specified nationalities across five activities. When aggregating across activities and models, results showed that 28.4% of the images depicted individuals wearing traditional attire, including attire that is impractical for the specified activities in several cases. This pattern was statistically significantly associated with regions, with the Middle East & North Africa and Sub-Saharan Africa disproportionately affected, and was also associated with World Bank income groups. Similar region- and income-linked patterns were observed for images labeled as depicting impractical attire in two athletics-related activities. To assess image-text alignment, CLIP, ALIGN, and GPT-4.1 mini were used to score 9,270 image-prompt pairs. Images labeled as featuring traditional attire received statistically significantly higher alignment scores when prompts included country names, and this pattern weakened or reversed when country names were removed. Revised prompt analysis showed that one model frequently inserted the word "traditional" (50.3% for traditional-labeled images vs. 16.6% otherwise). These results indicate that these representational patterns can be shaped by several components of the pipeline, including image generator, evaluation models, and prompt revision. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Computers and Society (cs.CY) Cite as: arXiv:2504.06313 [cs.CV] (or arXiv:2504.06313v5 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2504.06313 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Abdulkareem Alsudais [ view email ] [v1] Tue, 8 Apr 2025 05:37:06 UTC (660 KB) [v2] Sat, 12 Apr 2025 04:37:55 UTC (659 KB) [v3] Wed, 25 Jun 2025 19:48:46 UTC (854 KB) [v4] Tue, 30 Dec 2025 13:55:20 UTC (1,285 KB) [v5] Sat, 11 Apr 2026 08:23:59 UTC (1,485 KB) Full-text links: Access Paper: View a PDF of the paper titled Text-to-Image Models and Their Representation of People from Different Nationalities Engaging in Activities, by Abdulkareem Alsudais View PDF view license Current browse context: cs.CV < prev | next > new | recent | 2025-04 Change to browse by: cs cs.CY References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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