Imagining the Far East: Exploring Perceived Biases in AI-Generated Images of East Asian Women
Open accessAbstract
Image-generating AI, which allows users to create images from text, is increasingly used to produce visual content.Despite its advancements, cultural biases in AI-generated images have raised significant concerns.While much research has focused on issues within Western contexts, our study examines the perceived biases regarding the portrayal of East Asian women.In this exploratory study, we invited East Asian users to audit three popular models (DALL-E, Midjourney, Stable Diffusion) and identified 18 specific perceived biases, categorized into four patterns: Westernization, overuse or misuse of cultural symbols, sexualization & feminization, and racial stereotypes.This work highlights the potential challenges posed by AI models in portraying Eastern individuals.
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Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups, including people with disabilities.
“Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups [15, 21, 36, 41, 42, 45, 53, 66, 72, 91], including PwD [10, 12, 39, 48, 54, 62, 77, 113].”
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Cites
Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
(2020) · doi:10.1145/3313831 · Cited by 1,951 · see citing works
Facial attractiveness: evolutionary based research
Little, A. C., Jones, B. C., & DeBruine, L. M. (2011) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2010.0404 · Cited by 1,017 · see citing works
Lambrecht, A., & Tucker, C. (2019) · Management Science · doi:10.1287/mnsc.2018.3093 · Cited by 854 · see citing works
Importance of physical attractiveness in dating behavior.
Walster, E., Aronson, V., Abrahams, D., & Rottman, L. (1966) · Journal of Personality and Social Psychology · doi:10.1037/h0021188 · Cited by 848 · see citing works
Cunningham, M. R., Roberts, A., Barbee, A. P., Druen, P. B., & Wu, C. (1995) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.68.2.261 · Cited by 833 · see citing works
Trustworthy Artificial Intelligence: A Review
Kaur, D., Uslu, S., Rittichier, K. J., & Durresi, A. (2023) · ACM Computing Surveys · doi:10.1145/3491209 · Cited by 709 · see citing works
Physical attractiveness and dating choice: A test of the matching hypothesis
Berscheid, E., Dion, K. K., Walster, E., & Walster, G. W. (1971) · Journal of Experimental Social Psychology · doi:10.1016/0022-1031(71)90065-5 · Cited by 553 · see citing works
Generative artificial intelligence, human creativity, and art
Zhou, E., & Lee, D. (2024) · PNAS Nexus · doi:10.1093/pnasnexus/pgae052 · Cited by 459 · see citing works
Sociotechnical Harms of Algorithmic Systems: Scoping a Taxonomy for Harm Reduction
Shelby, R., Rismani, S., Henne, K., Moon, A., Rostamzadeh, N., Nicholas, P., Yilla-Akbari, N., Gallegos, J., Smart, A., Garcia, E., et al. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604673 · Cited by 279 · see citing works
Detecting price and search discrimination on the internet
Mikians, J., Gyarmati, L., Erramilli, V., & Laoutaris, N. (2012) · Proceedings of the 11th ACM Workshop on Hot Topics in Networks · doi:10.1145/2390231.2390245 · Cited by 244 · see citing works
Roselli, D., Matthews, J., & Talagala, N. (2019) · Companion Proceedings of The 2019 World Wide Web Conference · doi:10.1145/3308560.3317590 · Cited by 237 · see citing works
Jung, J., & Lee, S. (2006) · Family and Consumer Sciences Research Journal · doi:10.1177/1077727x06286419 · Cited by 223 · see citing works
Marchetti, G. (1994) · University of California Press eBooks · doi:10.1525/9780520914629 · Cited by 216 · see citing works
Bias detectives: the researchers striving to make algorithms fair
Courtland, R. (2018) · Nature · doi:10.1038/d41586-018-05469-3 · Cited by 205 · see citing works
Problematic Machine Behavior: A Systematic Literature Review of Algorithm Audits
Bandy, J. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3449148 · Cited by 193 · see citing works
Borrowed Power: Essays on Cultural Appropriation.
Root, D., Ziff, B., & Rao, P. V. (1998) · Journal of the Royal Anthropological Institute · doi:10.2307/3034856 · Cited by 186 · see citing works
How AI can distort human beliefs
Kidd, C., & Birhane, A. (2023) · Science · doi:10.1126/science.adi0248 · Cited by 151 · see citing works
Sun, L., Wei, M., Sun, Y., Suh, Y. J., Shen, L., & Yang, S. (2023) · Journal of Computer-Mediated Communication · doi:10.1093/jcmc/zmad045 · Cited by 146 · see citing works
A systematic review of socio-technical gender bias in AI algorithms
Hall, P., & Ellis, D. (2023) · Online Information Review · doi:10.1108/oir-08-2021-0452 · Cited by 117 · see citing works
The Chan's great continent: China in Western minds
(1999) · Choice Reviews Online · doi:10.5860/choice.36-4053 · Cited by 108 · see citing works
Stressful Experiences of Masculinity Among U.S.-Born and Immigrant Asian American Men
Lu, A., & Wong, Y. J. (2013) · Gender & Society · doi:10.1177/0891243213479446 · Cited by 104 · see citing works
DeVos, A., Dhabalia, A., Shen, H., Holstein, K., & Eslami, M. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517441 · Cited by 98 · see citing works
Beauty perception: A historical and contemporary review
Dimitrov, D., & Kroumpouzos, G. (2023) · Clinics in Dermatology · doi:10.1016/j.clindermatol.2023.02.006 · Cited by 83 · see citing works
Azhar, S., Alvarez, A. R. G., Farina, A. S. J., & Klumpner, S. (2021) · Affilia · doi:10.1177/08861099211001460 · Cited by 83 · see citing works
Crowd-assisted search for price discrimination in e-commerce: first results
Mikians, J., Gyarmati, L., Erramilli, V., & Laoutaris, N. (2013) · Proceedings of the ninth ACM conference on Emerging networking experiments and technologies · doi:10.1145/2535372.2535415 · Cited by 82 · see citing works
“They only care to show us the wheelchair”: disability representation in text-to-image AI models
Mack, K. A., Qadri, R., Denton, R., Kane, S. K., & Bennett, C. L. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642166 · Cited by 67 · see citing works
McNeill, W. H. (1997) · Orbis · doi:10.1016/s0030-4387(97)90002-8 · Cited by 66 · see citing works
Asian America through the lens: history, representations, and identity
(1999) · Choice Reviews Online · doi:10.5860/choice.36-3247 · Cited by 60 · see citing works
Hamilton, M. (2019) · Behavioral Sciences & the Law · doi:10.1002/bsl.2406 · Cited by 58 · see citing works
Investigating Gender and Racial Biases in DALL-E Mini Images
Cheong, M., Abedin, E., Ferreira, M., Reimann, R., Chalson, S., Robinson, P., Byrne, J., Ruppanner, L., Alfano, M., & Klein, C. (2024) · ACM Journal on Responsible Computing · doi:10.1145/3649883 · Cited by 57 · see citing works
Plato’s Shadows in the Digital Cave: Controlling Cultural Bias in Generative AI
Karpouzis, K. C. (2024) · Electronics · doi:10.3390/electronics13081457 · Cited by 52 · see citing works
Power and politics in framing bias in Artificial Intelligence policy
Ulnicane, I., & Aden, A. (2023) · Review of Policy Research · doi:10.1111/ropr.12567 · Cited by 50 · see citing works
Lu, J. G., Nisbett, R. E., & Morris, M. W. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2118244119 · Cited by 45 · see citing works
Beyond Whiteness: Perspectives on the Rise of the Pan-Asian Beauty Ideal
Yip, J., Ainsworth, S., & Hugh, M. T. (2019) · Springer eBooks · doi:10.1007/978-3-030-11711-5_5 · Cited by 44 · see citing works
Han, M. (2003) · Communication Studies · doi:10.1080/10510970309363266 · Cited by 42 · see citing works
Southeast Asia in World History
Lockard, C. A. (2009) · doi:10.1093/oso/9780195160758.001.0001 · Cited by 41 · see citing works
Beyond Algorithmic Bias: A Socio-Computational Interrogation of the Google Search by Image Algorithm
Papakyriakopoulos, O., & Mboya, A. M. (2022) · Social Science Computer Review · doi:10.1177/08944393211073169 · Cited by 23 · see citing works
Riddick, J. F. (2006) · Praeger eBooks · doi:10.5040/9798400664519 · Cited by 11 · see citing works
Personality and Physical Attractiveness
Liu, J. (2020) · Springer eBooks · doi:10.1007/978-3-319-24612-3_715 · Cited by 3 · see citing works
Flagged first, then by citations
1 of the 40 references couldn’t be matched to a known paper.
Research works printed but not linked: 6
- Leah Cashin. 2024. The Evolution of Chinese Beauty. Articulāte 29, 1 (2024), 6.
- Rachel Courtland. 2018. The bias detectives. Nature 558, 7710 (2018), 357-360.
- Sourojit Ghosh and Aylin Caliskan. 2023. 'Person' == Light-skinned, Western Man, and Sexualization of Women of Color: Stereotypes in Stable Diffusion. In Conference on Empirical Methods in Natural Language Processing. ACL, 6971-- 6985.
- Everypixel Journal. 2023. AI Has Already Created As Many Images As Photog- raphers Have Taken in 150 Years. Statistics for 2023. https://journal.everypixel. com/ai-image-statistics. Last accessed: Sept 1, 2024.
- Safiya Umoja Noble. 2018. Algorithms of oppression: How search engines reinforce racism. New York University Press.
- Traise Yamamoto. 2000. In/visible difference: Asian American women and the politics of spectacle. Race, Gender & Class 7, 1 (2000), 43-55.
Cited by
Sorted by citations
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Chamma, D., Ahmadpour, N., Ahmed, S. I., Mim, N. J., Zhang, W. Q., di Bona, K., Sachathep, T., Horst, H. A., & Harb, J. I. (2025) · Australian Conference on Human-Computer Interaction (OzCHI) · doi:10.1145/3764687.3767279 · Cited by 5 · see citing works
Social stereotypes in AI text-to-image generation
Barve, S., Mao, A., Shi, J. M., Juneja, P., & Saha, K. (2026) · AI and Ethics · doi:10.1007/s43681-026-01146-8 · Cited by 9 · see citing works
Engaging Communities Meaningfully in Defining Disability Representation for AI Image Generation
Thieme, A., Faia Marques, R., Grayson, M., Balachandar, S., Tyler Cassidy, C., Choksi, M. Z., Longden, C., Huda, R. S., Ileve Kalovwe, N., Mallon, C., et al. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790768 · Cited by 1 · see citing works
“Generative AI outputs tend to reproduce normative views and mirror societal biases for marginalized groups [15, 21, 36, 41, 42, 45, 53, 66, 72, 91], including PwD [10, 12, 39, 48, 54, 62, 77, 113].”
Kim, S., Jo, Y., Na, S., Lim, H., Lee, E., Choi, Y. M., Cho, S., & Hong, H. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790820 · Cited by 2 · see citing works
Exploring Teenagers’ Trust in AI Chatbots: An Empirical Study of Chinese Middle-School Students
Qiu, S., Lin, A., Wang, S., & Lan, X. (2025) · International Conference on Human-Engaged Computing (ICHEC) · doi:10.1145/3786995.3787019 · Cited by 4 · see citing works
Self-citationDoh, M., Gulati, A., Canali, C., & Oliver, N. (2026) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3805689.3806810 · Cited by 1 · see citing works
YAPAY ZEKÂ TARAFINDAN ÜRETİLEN GÖRSELLERDE BASMAKALIPLAR: TÜRKİYE TEMSİLİ ÜZERİNE BİR İNCELEME
TAŞDELEN, H., & ALTIN, Ş. Y. (2026) · Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.35379/cusosbil.1695903
Contextual and Cultural Limitations of Text-to-Image Generators: The Case of East Asia
Kim, K. (2026) · Springer eBooks · doi:10.1007/978-3-032-21685-4_14
- Authors
- Lan, X., An, J., Guo, Y., Chiyou, T., Cai, X., & Zhang, J.
- Affiliations
- Fudan University (CN)
- Fudan University, Shanghai, China
- University of California, Los Angeles (US)
- Hong Kong Polytechnic University (HK)
- Venue
- Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2025-04-25
- Pages
- 1-7
- OpenAlex
- W4409887240
- Cited by
- 8
- References
- 40
- Citations by year
- 2026: 6 · 2025: 2
- FWCI
- 8.26
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- NSFC (62402121)
- School of Journalism, Fudan University (Research and Innovation Project)
- Shanghai Municipal Education Commission (Chenguang Project)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Gender Studies › Media, Gender, and Advertising
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- AI-generated images, East Asian women, cultural bias, racial stereotypes, sexualization, feminization, Westernization, cultural symbols, DALL-E, Midjourney, Stable Diffusion, user perception
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