Gender bias in text-to-image generative artificial intelligence: Neglect and stereotypical presentations across three popular platforms

Abstract

Images generated by artificial intelligence (AI) were assumed to underrepresent women and to contain stereotypical portrayals. A total of 1344 images were generated at two times of measurement by DALL·E, Midjourney, and Stable Diffusion and were analyzed via a preregistered content analysis. Results revealed representational and presentational bias, varying between prompts and between AI platforms. Women were depicted less frequently than men in images generated with the prompt “competent person,” whereas women were generated more often with a neutral prompt (i.e. “person”) or with the prompt “warm person.” Regarding stereotypical presentations, images representing women (vs men) showed lower facial prominence (face-ism), higher intensity smiles, and more pronounced lateral head tilts (head canting). These biases varied significantly between AI platforms. The findings suggest that gender stereotypes are spread by generative AI systems and highlight the need for interventions in the development and deployment of image-generating AI.