Unveiling Bias: Analyzing Race and Gender Disparities in AI-Generated Imagery

David Cordero, Yijun Zhao, Priscilla Diaz, Gary M. Weiss

Abstract: This study explores gender and racial bias in AI-generated images. DALL-E 3 was used to generate 2800 images based on prompts related to occupations, activities, and positive/negative personal characteristics, and human reviewers classified the generated images by gender and race. Our analysis reveals that certain prompts are disproportionately associated with specific races and/or genders, suggesting that the AI model may be biased. Race and gender statistics are compared with real-world statistics to determine whether the generated images mirror existing societal biases or introduce new biases. Our findings raise ethical concerns about fairness and representation in AI technologies and discuss the consequences of biased image generation. This research is motivated by the growing integration of AI in media generation and the associated risks of perpetuating and amplifying existing biases. The dataset used in this study is provided via a GitHub repository to support reproducibility, transparency, and broader studies in the research community.

External IDs:dblp:conf/compsac/CorderoZDW25