Body representations in AI-generated images in physical and sports contexts: biases, stereotypes, and critical reflections
DOI:
https://doi.org/10.47197/retos.v71.116433Keywords:
actividad físico-deportiva, diversidad corporal, estereotipo y sesgo corporal, inteligencia artificial generativa, representación corporalAbstract
Introduction: Every day, millions of images generated by generative artificial intelligence (AI) platforms are produced, distributed, and used across personal and professional domains.
Objective: This article analyzes bodily representations generated by three generative AI platforms in the context of physical and sports activities.
Methodology: Using a content analysis approach through structured co-observation, a total of 732 images were examined. These images were generated by three AI tools (Dall·E 3, Mid Journey, and Stable Diffusion) based on 60 neutral prompts describing various physical and sports activities, without reference to body-related or sociodemographic characteristics.
Results: The findings reveal the reproduction of hegemonic and systemic biases and stereotypes related to the body, despite the use of inclusive and neutral prompts. The AI-generated images predominantly portray normative bodies (mainly young, muscular, white, male figures) while reinforcing female stereotypes and rendering invisible other bodily realities associated with race, age, or disability.
Discussion: These results align with recent literature suggesting that artificial intelligence tools not only replicate existing societal biases and body stereotypes but also exacerbate them.
Conclusions: AI is both reproducing and amplifying pre-existing social biases and stereotypes. This highlights the need for reflective research that encourages a deeper, critical, and thoughtful examination of the responsible use of these technologies. It calls for progress toward fairer, more neutral, diverse, and inclusive AI models.
References
Abbott, B.D., & Barber, B.L. (2010). Embodied image: gender differences in functional and aesthetic body image among Australian adolescents. Body Image, 7(1), 22-31. https://doi.org/10.1016/j.bodyim.2009.10.004
Águila Soto, C., & López Vargas, J. J. (2019). Cuerpo, corporeidad y educación: una mirada reflexiva des-de la Educación Física. Retos, 35, 413–421. https://doi.org/10.47197/retos.v0i35.62035
Ali R., Tang, O.Y., Connolly, I.D., Abdulrazeq H.F., Mirza F.N., Lim R.K., Johnston B.R., Groff M.W., Wi-lliamson T., Svokos K., Libby T.J., Shin J.H., Gokaslan Z.L., Doberstein C.E., Zou J., & Asaad W.F. (2024). Demographic representation in 3 leading artificial intelligence text-to-image generators, JAMA Surgery, 159(1), 87-95. https://doi.org/10.1101/2023.05.24.23290463
Borau, S. (2025). Deception, discrimination, and objectification: ethical issues of female AI Agents. J Bus Ethics, 198,1–19. https://doi.org/10.1007/s10551-024-05754-4
Borji, A. (2023). Generated faces in the wild: Quantitative comparison of Stable Diffusion, Mid Journey and DALL-E 2. https://arxiv.org/abs/2210.00586
Bourdieu, P. (1998). La domination masculine. Paris, Éditions de Seuil.
Butler, J. (2004). Undoing gender. New York, NY: Routledge.
Cheong, M., Abedin, E., Ferreira, M., Reimann, R., Chalson, S., Robinson, P., Byrne, J., Ruppanner, L., Alfano, M., & Klein, C. (2024). Investigating gender and racial biases in DALL-E Mini Images. ACM Journal on Responsible Computing, 1, 1-20. https://doi.org/10.1145/3649883
Collett, C., Neff, G., & Gouvea Gomes, L. (2022). Los efectos de la IA en la vida laboral de las mujeres. Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura (UNESCO), Banco Interamericano de Desarrollo (BID) y Organización para la Cooperación y el Desarrollo Eco-nómicos (OCDE). https://wp.oecd.ai/app/uploads/2022/03/Los-efectos-de-la-IA-en-la-vida-laboral-de-las-mujeres.pdf.
Díaz-Barahona, J., Valverde, T., & Moya, I. (2022). Estereotipos asociados al cuerpo humano: análisis de aplicaciones móviles usadas en la educación físico-deportiva. Pixel-Bit, Revista de medios y educación, (64), 79–103. https://doi.org/10.12795/pixelbit.90297
Díaz-Barahona, J., Valverde-Esteve, T., & Moya-Mata, I. (2023). Diseño y validación de un instrumento para analizar los estereotipos corporales representados en el software y aplicaciones digitales (apps). Retos, 47, 69-77. https://doi.org/10.47197/retos.v47.94899
Every Pixel. (2023, August 15). AI image statistics for 2024: How much content was created by AI. Eve-rypixel Journal. https://journal.everypixel.com/ai-image-statistics
Fernández, A., & Garrido-Merchán, E. (2025). A taxonomy of the biases of the images created by genera-tive artificial intelligence. Curr Trends Business Mgmt, 3(1), 01-10.
Franganillo, J. (2022). Contenido generado por inteligencia artificial: oportunidades y amenazas. Anua-rio ThinkEPI, (16), 16-24. https://doi.org/10.3145/thinkepi.2022.e16a24
Freire-Sánchez, A., Fitó-Carreras, M., Vidal-Mestre, M., & Barra-Pérez, D. (2024). Diseño y representa-ción visual en la IA generativa de la salud mental. Gráfica, (391), 1-9. https://doi.org/10.5565/rev/grafica.391
García-Ull, FJ., & Melero-Lázaro, M. (2023). Gender stereotypes in AI-generated images. Profesional de la información, (32)5. https://doi.org/10.3145/epi.2023.sep.05
Gengler, E. (2024). Sexism, racism, and classism: social biases in text-to-image generative AI in the con-text of power, success, and beauty. Wirtschaftsinformatik 2024. https://aisel.aisnet.org/wi2024/48
Gestos, M., Smith-Merry J., & Campbell, A. (2018). Representation of women in video games: a system-atic review of literature in consideration of adult female wellbeing. Cyberpsychology, Behavior, and Social Networking, 21(9), 535-541. https://doi.org/10.1089/cyber.2017.0376
Gisselbaek, M., Suppan, M., Minsart, L., Köselerli, E., Nainan, S., Matot, I., Barreto, O. L., Saxena, S., & Ber-ger-Estilita, J. (2024). Representation of intensivists' race/ethnicity, sex, and age by artificial in-telligence: a cross-sectional study of two text-to-image models. Critical care, 28(1), 363. https://doi.org/10.1186/s13054-024-05134-4
González, A., Táboas, M. I., & Rey, A. (2010). Los libros de texto como herramientas para la promoción de una práctica físico-deportiva en igualdad: análisis comparativo de la representación racial entre los libros publicados durante la vigencia de la LOGSE y la LOE. Cuadernos de psicología del deporte, 10(2), 31-36.
Heaven, W. D. (2024, Jan). 6 big questions for generative AI. MIT Technology Review, 127, 30-37. https://www.proquest.com/magazines/6-big-questions-generative-ai/docview/1937358610/se-2
Heinemann, K. (2003). Introducción a la metodología de la investigación empírica en las ciencias del deporte. Paidotribo, Barcelona.
Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biome-trics, 33(1), 159–174.
Linares-Lanzman, J., Stypińska, J., & Rosales, A. (2025). Edadismo generativo: cuando la IA generativa refuerza los estereotipos de edad. Revista de estudios de ciencias de la información y la comu-nicación COMeIN. (151). https://doi.org/10.7238/c.n151.2513
MacDonald, J. P., Pape, M., Ackerman, K. E., Carneiro, E., Huang, Y., Rizzone, K. H., & Mountjoy, M. (2024). The digital mirror: how generative artificial intelligence reflects and amplifies gender bias. British Journal of Sports Medicine, (59)207-209. https://doi.org/10.1136/bjsports-2024-108998
Mack, K. A., Qadri, R., Denton, R., Kane, S. K., & Bennett, C. L. (2024). They only care to show us the wheelchair: Disability representation in text-to-image AI models. En Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), 11–16 de mayo de 2024, Hono-lulu, HI, EE.UU. ACM. https://doi.org/10.1145/3613904.3642166
Martínez-Bello, V., Cabrera, Y., Díaz-Barahona, J., & Bernabé-Villodre, M., (2021). Bodies in the early childhood education classroom: a Bourdieusian analysis of curricular materials, Sports, Educa-tion and Society, 26(1), 29-44. https://doi.org/10.1080/13573322.2019.1690442
Moya-Mata, I., Ruiz, L., Martín, J., Pérez, P.M., & Ros, C. (2017). La representación de la discapacidad en las imágenes de los libros de texto de Educación Física: ¿inclusión o exclusión? Retos, 32, 88–95. https://doi.org/10.47197/retos.v0i32.52191
Moya-Mata, I., Ruiz-Sanchis, L., Martín-Ruiz, J., & Ros, C. (2019). La actividad física representada en los libros de texto de educación física en educación primaria. Movimento, 25, 1-13. https://doi.org/10.22456/1982-8918.89296
Neuendorf, K.A. (2011). Content analysis: A methodological primer for gender research. Sex Roles, 64(3-4), 276-289. https://doi.org/10.1007/s11199-010-9893-0
Nicoletti, L., & Bass, D. (2023, June 9). Generative AI takes stereotypes and bias from bad to worse. Bloomberg. https://www.bloomberg.com/graphics/2023-generative-ai-bias/
Park, Y. S. (2024). White Default: Examining Racialized Biases Behind AI-Generated Images. Art Educa-tion, 77(4), 36–45. https://doi.org/10.1080/00043125.2024.2330340.
Pedro, R., Lucio, A., Torrão, R., & Tognoli, N., (2024). Datafication, artificial intelligence and images: the dominant paradigm in the representation of knowledge in images. Knowledge Organization, 51 (2), 117-126. https://doi.org/10.5771/0943-7444-2024-2-117
Rapp, A., Di Lodovico, C., Torrielli, F., & Di Caro, L. (2025). How do people experience the images creat-ed by generative artificial intelligence? An exploration of people's perceptions, appraisals, and emotions related to a Gen-AI text-to-image model and its creations. International Journal of Human-Computer Studies, 193, https://doi.org/10.1016/j.ijhcs.2024.103375
Rice C., Riley S., LaMarre A., & Bailey K.A. (2021). What a body can do: rethinking body functionality through a feminist materialist disability lens. Body Image, 38, 95-105, https://doi.org/10.1016/j.bodyim.2021.03.014
Rivera, J.G. (2024). Inteligencias artificiales generativas 2024 (2.ª ed.). Red Educativa Digital Descartes, Córdoba.
Rodríguez, J., Sanmiguel-Rodríguez, A., & Álvarez-Seoane, D. (2018). Revisión bibliográfica en el contex-to español sobre investigaciones relacionadas con los libros de texto y materiales didácticos en educación física. Retos, 34, 363-370. https://doi.org/10.47197/retos.v0i34.65902
Sandoval-Martín, T., & Martínez-Sanzo, E. (2024). Perpetuation of gender bias in visual representation of professions in the generative AI tools DALL·E and Bing Image Creator. Social Sciences, 13(5), 250. https://doi.org/10.3390/socsci13050250
Santoniccolo F., Trombetta T., Paradiso M.N., & Rollè L. (2023). Gender and media representations: a review of the literature on gender stereotypes, objectification and sexualization. International Journal of Environmental Research and Public Health, 20 (10)5770, https://doi.org/10.3390/ijerph20105770
Stoyanov, S.R., Hides, L., Kavanagh, D.J., Zelenko, O., Tjondronegoro, D., & Mani, M. (2015). Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR mHealth and uHealth, 3(1), 27. https://doi.org/10.2196/mhealth.3422
UNESCO. (2024). Challenging systematic prejudices: an investigation into bias against women and girls in large language models. UNESCO, Paris.
Vilanou, C. (2001). Imágenes del cuerpo humano. Apunts. Educación Física y Deportes, 63, 94-104.
Wu, Y., Nakashima, Y., & Garcia, N. (2025). Revealing gender bias from prompt to image in stable diffu-sion. Journal of Imaging, 11(2), 35. https://doi.org/10.3390/jimaging11020035
Yue, M., & Yang, B. (2024). The gender show in “Mid Journey”: the stubborn biases and gender fantasies of ai painting tools. Communications in Humanities Research, (33), 152-159. https://doi.org/10.54254/2753-7064/33/20240080
Zhou, M., Abhishek, V., Derdenger, T. P., Kim, J., & Srinivasan, K. (2024). Bias in generative AI. https://arxiv.org/abs/2403.02726
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 José Díaz Barahona, Pablo Marín Liébana
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and ensure the magazine the right to be the first publication of the work as licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of authorship of the work and the initial publication in this magazine.
- Authors can establish separate additional agreements for non-exclusive distribution of the version of the work published in the journal (eg, to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Is allowed and authors are encouraged to disseminate their work electronically (eg, in institutional repositories or on their own website) prior to and during the submission process, as it can lead to productive exchanges, as well as to a subpoena more Early and more of published work (See The Effect of Open Access) (in English).
This journal provides immediate open access to its content (BOAI, http://legacy.earlham.edu/~peters/fos/boaifaq.htm#openaccess) on the principle that making research freely available to the public supports a greater global exchange of knowledge. The authors may download the papers from the journal website, or will be provided with the PDF version of the article via e-mail.
