The gendered dress of DALL-E 2: Exploring profession-based images in the Indian context

Authors

DOI:

https://doi.org/10.7146/mk.v40i76.143565

Keywords:

DALL-E 2, profession, dress, gender, India

Abstract

This study delves into the intricate realm of gender and artificial intelligence (AI) through an examination of DALL-E 2 generated images within the Indian context. The study takes a methodological approach that focuses on assessing images generated in response to prompts such as ‘a farmer cultivating crops in rural Punjab’ or ‘a nurse providing care in a hospital in Delhi’ to reveal the dynamics of gender performativity within profession-based visual content. The generated images were analysed using the dress as a phenomenon to visualise Indian man, Indian woman and ambiguous Indian. The study concludes that DALL-E 2’s algorithm reiterates the binary gender norms leaving no space for ambiguous Indian in its responses. Though the generated image is centred on female professions, it is largely employed by male surroundings. Hindu religious symbols are largely used among male professionals denoting its predominance. In its aesthetics, the individuals portrayed exhibit a brown complexion, echoing the demographic landscape irrespective of gender.

References

Acharya, A. (2023). OpenAI’s DALL-E 3 Explained: Generate Images with ChatGPT. https://encord.com/blog/openai-dall-e-3-what-we-know-so-far/

Adomaitis, A. D., Saiki, D., Johnson, K. K. P., Sahanoor, R., & Attique, A. (2024). Relationships Between Dress and Gender Identity: LGBTQIA +. Clothing and Textiles Research Journal, 42(1), 3–18. https://doi.org/10.1177/0887302X211059103

Anand, T., Chauhan, A., Jauhari, T., Shah, A., Singh, R., Liang, B., & Dutta, R. (2023). Identifying Race and Gender Bias in Latent Diffusion AI Image Generation (SSRN Scholarly Paper 4602033). https://doi.org/10.2139/ssrn.4602033

Arruzza, C. (2015). Gender as Social Temporality: Butler (and Marx). Historical Materialism, 23(1), 28–52. https://doi.org/10.1163/1569206X-12341396

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610–623. https://doi.org/10.1145/3442188.3445922

Butler, J. (1990). Gender trouble: Feminism and the subversion of identity (Li. J. Nicholson, Ed.). Routledge.

Butler, J. (2004). Undoing gender. Routledge. https://doi.org/10.4324/9780203499627

Chakravarti, U. (1993). Conceptualising Brahmanical Patriarchy in Early India. Economic and Political Weekly, 28(14), 579-585.

Cho, J., Zala, A., & Bansal, M. (2023). DALL-Eval: Probing the Reasoning Skills and Social Biases of Text-to-Image Generation Models. 3043–3054. https://doi.org/10.1109/ICCV51070.2023.00283

Clarke, H. M. (2020). Gender Stereotypes and Gender-Typed Work. In K. F. Zimmermann (Ed.), Handbook of Labor, Human Resources and Population Economics (pp. 1–23). Springer International Publishing. https://doi.org/10.1007/978-3-319-57365-6_21-1

Deleuze, G., & Guattari, F. (2004). A thousand plateaus: Capitalism and schizophrenia. Continuum.

Drudy, S. (2011). Gender balance/gender bias: The teaching profession and the impact of feminisation. In Gender Balance and Gender Bias in Education. Routledge.

Eccles, J. S., Jacobs, J. E., & Harold, R. D. (1990). Gender Role Stereotypes, Expectancy Effects, and Parents’ Socialization of Gender Differences. Journal of Social Issues, 46(2), 183–201. https://doi.org/10.1111/j.1540-4560.1990.tb01929.x

Fox, L. B., & Barth, J. M. (2017). The Effect of Occupational Gender Stereotypes on Men’s Interest in Female-Dominated Occupations. Sex Roles, 76(7), 460–472. https://doi.org/10.1007/s11199-016-0673-3

Garcí­a-Ull, F.-J., & Melero-Lázaro, M. (2023). Gender stereotypes in AI-generated images. Profesional de la información / Information Professional, 32(5), Article 5. https://doi.org/10.3145/epi.2023.sep.05

Gorska, A. M., & Jemielniak, D. (2023). The invisible women: Uncovering gender bias in AI-generated images of professionals. Feminist Media Studies, 0(0), 1–6. https://doi.org/10.1080/14680777.2023.2263659

Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533

Jeevanandam, N. (2022). Dall-E - the AI that creates images of everything. INDIAai. https://Indiaai.gov.in/article/dall-e-the-ai-that-creates-images-of-everything

Jeffery, P., & Jeffery, R. (1996). Don’t marry me to a plowman!: Women’s everyday lives in rural North India. Westview Press.

Jeffreys, S. (2011). Man’s Dominion: The Rise of Religion and the Eclipse of Women’s Rights. Routledge. https://doi.org/10.4324/9780203802397

Jonnergård, K., Stafsudd, A., & Elg, U. (2010). Performance Evaluations as Gender Barriers in Professional Organizations: A Study of Auditing Firms. Gender, Work & Organization, 17(6), 721–747. https://doi.org/10.1111/j.1468-0432.2009.00488.x

Kaiser, S. B. (2012). Fashion and Cultural Studies. Berg.

Koene, J. M. (2017). Sex determination and gender expression: Reproductive investment in snails. Molecular Reproduction and Development, 84(2), 132–143. https://doi.org/10.1002/mrd.22662

Li, H. (2019). Special Section Introduction: Artificial Intelligence and Advertising. Journal of Advertising. https://www.tandfonline.com/doi/abs/10.1080/00913367.2019.1654947

Lovaas, K. E., & Jenkins, M. M. (2007). Sexualities and Communication in Everyday Life: A Reader. SAGE.

Mannering, H. (2023). Analysing Gender Bias in Text-to-Image Models using Object Detection (arXiv:2307.08025). arXiv. https://doi.org/10.48550/arXiv.2307.08025

Miller, D. (2010). Stuff. Polity.

Miller-Spillman, K. A., & Reilly, A. (2019). The Meanings of Dress. Bloomsbury Publishing USA. https://doi.org/10.5040/9781501323904

Mirza, H. S. (2013). ‘A second skin’: Embodied intersectionality, transnationalism and narratives of identity and belonging among Muslim women in Britain. Women’s Studies International Forum, 36, 5–15. https://doi.org/10.1016/j.wsif.2012.10.012

Naik, R., & Nushi, B. (2023). Social Biases through the Text-to-Image Generation Lens. Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society, 786–808. https://doi.org/10.1145/3600211.3604711

OpenAI. (2022). DALL·E now available without waitlist. https://openai.com/blog/dall-e-now-available-without-waitlist

Ouchchy, L., Coin, A., & Dubljević, V. (2020). AI in the headlines: The portrayal of the ethical issues of artificial intelligence in the media. AI & SOCIETY, 35(4), 927–936. https://doi.org/10.1007/s00146-020-00965-5

Paul, M. (2022). All that you need to now about Dall-E. https://www.telegraphindia.com/science-tech/all-that-you-need-to-now-about-dall-e/cid/1871017

Probert, B. (2005). ‘I Just Couldn’t Fit It In’: Gender and Unequal Outcomes in Academic Careers. Gender, Work & Organization, 12(1), 50–72. https://doi.org/10.1111/j.1468-0432.2005.00262.x

Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical Text-Conditional Image Generation with CLIP Latents (arXiv:2204.06125). arXiv. https://doi.org/10.48550/arXiv.2204.06125

Roach-Higgins, M. E., & Eicher, J. B. (1992). Dress and Identity. Clothing and Textiles Research Journal, 10(4), 1–8. https://doi.org/10.1177/0887302X9201000401

Rocha, V., & van Praag, M. (2020). Mind the gap: The role of gender in entrepreneurial career choice and social influence by founders. Strategic Management Journal, 41(5), 841–866. https://doi.org/10.1002/smj.3135

Roza, N. (2023, September 2). DALL-E Statistics 2023: Stats Facts and Trends for Today! https://nikolaroza.com/dall-e-statistics-facts-trends/

Sambasivan, N., Arnesen, E., Hutchinson, B., Doshi, T., & Prabhakaran, V. (2021). Re-imagining Algorithmic Fairness in India and Beyond. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 315–328. https://doi.org/10.1145/3442188.3445896

Shukla, P. (2015a). Costume: Performing identities through dress. Indiana University Press.

Shukla, P. (2015b). The Grace of Four Moons: Dress, Adornment, and the Art of the Body in Modern India. Indiana University Press.

Weidinger, L., Mellor, J., Rauh, M., Griffin, C., Uesato, J., Huang, P.-S., Cheng, M., Glaese, M., Balle, B., Kasirzadeh, A., Kenton, Z., Brown, S., Hawkins, W., Stepleton, T., Biles, C., Birhane, A., Haas, J., Rimell, L., Hendricks, L. A., … Gabriel, I. (2021). Ethical and social risks of harm from Language Models (arXiv:2112.04359). arXiv. https://doi.org/10.48550/arXiv.2112.04359

Wesolowicz, D. M., Clark, J. F., Boissoneault, J., & Robinson, M. E. (2018). The roles of gender and profession on gender role expectations of pain in health care professionals. Journal of Pain Research, 11, 1121–1128. https://doi.org/10.2147/JPR.S162123

Zhao, J., Zhou, Y., Li, Z., Wang, W., & Chang, K.-W. (2018). Learning Gender-Neutral Word Embeddings. In E. Riloff, D. Chiang, J. Hockenmaier, & J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (pp. 4847–4853). Association for Computational Linguistics. https://doi.org/10.18653/v1/D18-1521

Downloads

Published

2024-08-30

How to Cite

Mubashir, M. (2024). The gendered dress of DALL-E 2: Exploring profession-based images in the Indian context. MedieKultur: Journal of Media and Communication Research, 40(76), 100–119. https://doi.org/10.7146/mk.v40i76.143565

Issue

Section

Articles: Theme section