Sexism, Racism, and Classism: Social Biases in Text-to-Image Generative AI in the Context of Power, Success, and Beauty

Eva Johanna Gengler ()
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Eva Johanna Gengler: FAU Erlangen-Nürnberg, Institute of Information Systems & International Doctorate Programme Business and Human Rights

A chapter in People, Society, and Ethical Challenges of Information Systems, 2026, pp 117-133 from Springer

Abstract: Abstract This study examines the manifestations of sexism, racism, and classism in the output of six text-to-image generative AI systems within the constructs of power, success, and beauty. A total of 180 images were generated using three prompts for each AI tool. Our analysis focused on detecting gender, racial, and class biases, as well as age discrimination. The findings reveal an underrepresentation of women and People of Color across the generated images. Additionally, the tendency to depict women in a sexualized manner was prominent. Data also indicated a bias towards younger depictions of women relative to men and People of Color relative to white individuals. The images overwhelmingly represented individuals as belonging to a higher socioeconomic class, pointing towards a systemic bias within AI systems towards privilege.

Keywords: Generative AI; Text-to-image; Sexism; Racism; Classism (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-08486-6_9

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DOI: 10.1007/978-3-032-08486-6_9

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