The Digital Mirror: Gender Bias and Occupational Stereotypes in AI-Generated Images
This addresses harmful gender biases in AI-generated images, which is crucial for diverse representation in media and professional settings, though it is incremental as it builds on existing bias research.
The study tested gender bias in AI-generated images of occupations using DALL-E 3 and Ideogram, finding that both tools reinforce traditional gender stereotypes to varying degrees based on analysis of over 750 images.
Generative AI offers vast opportunities for creating visualisations, such as graphics, videos, and images. However, recent studies around AI-generated visualisations have primarily focused on the creation process and image quality, overlooking representational biases. This study addresses this gap by testing representation biases in AI-generated pictures in an occupational setting and evaluating how two AI image generator tools, DALL-E 3 and Ideogram, compare. Additionally, the study discusses topics such as ageing and emotions in AI-generated images. As AI image tools are becoming more widely used, addressing and mitigating harmful gender biases becomes essential to ensure diverse representation in media and professional settings. In this study, over 750 AI-generated images of occupations were prompted. The thematic analysis results revealed that both DALL-E 3 and Ideogram reinforce traditional gender stereotypes in AI-generated images, although to varying degrees. These findings emphasise that AI visualisation tools risk reinforcing narrow representations. In our discussion section, we propose suggestions for practitioners, individuals and researchers to increase representation when generating images with visible genders.