RT info:eu-repo/semantics/article
T1 Gender stereotypes in AI-generated images
T2 Estereotipos de género en imágenes generadas mediante inteligencia artificial
A1 Melero Lázaro, Mónica
A1 García Ull, Francisco José
K1 Inteligencia artificial
K1 Estereotipos de género
K1 DALL-E
K1 Diferencias de género
K1 Artificial intelligence
K1 Open AI
K1 Synthetic images
K1 Gender stereotypes
K1 Sex biases
K1 Professions
K1 1203.04 Inteligencia Artificial
K1 5206.09 Sexo
AB This study explores workplace gender bias in images generated by DALL-E 2, an application for synthesising images based on artificial intelligence (AI). To do this, we used a stratified probability sampling method, dividing the sample into segments on the basis of 37 different professions or prompts, replicating the study by Farago, Eggum-Wilkens and Zhang (2020) on gender stereotypes in the workplace. The study involves two coders who manually input different professions into the image generator. DALL-E 2 generated 9 images for each query, and a sample of 666 images was collected, with a confidence level of 99% and a margin of error of 5%. Each image was subsequently evaluated using a 3-point Likert scale: 1, not stereotypical; 2, moderately stereotypical; and 3, strongly stereotypical. Our study found that the images generated replicate gender stereotypes in the workplace. The findings presented indicate that 21.6% of AI-generated images depicting professionals exhibit full stereotypes of women, while 37.8% depict full stereotypes of men. While previous studies conducted with humans found that gender stereotypes in the workplace exist, our research shows that AI not only replicates this stereotyping, but reinforces and increases it. Consequently, while human research on gender bias indicates strong stereotyping in 35% of instances, AI exhibits strong stereotyping in 59.4% of cases. The results of this study emphasise the need for a diverse and inclusive AI development community to serve as the basis for a fairer and less biased AI.
PB EPI SL
SN 1699-2407
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/66375
UL https://uvadoc.uva.es/handle/10324/66375
LA spa
NO Profesional De La información Information Professional, 32(5).
DS UVaDOC
RD 19-nov-2024