Smiling Women Pitching Down: Auditing Representational and
  Presentational Gender Biases in Image Generative AI
Luhang Sun Mian Wei Yibing Sun Yoo Ji Suh Liwei Shen Sijia Yang
Generative AI models like DALL-E 2 can interpret textual prompts and generate
high-quality images exhibiting human creativity. Though public enthusiasm is
booming, systematic auditing of potential gender biases in AI-generated images
remains scarce. We addressed this gap by examining the prevalence of two
occupational gender biases (representational and presentational biases) in
15,300 DALL-E 2 images spanning 153 occupations, and assessed potential bias
amplification by benchmarking against 2021 census labor statistics and Google
Images. Our findings reveal that DALL-E 2 underrepresents women in
male-dominated fields while overrepresenting them in female-dominated
occupations. Additionally, DALL-E 2 images tend to depict more women than men
with smiling faces and downward-pitching heads, particularly in
female-dominated (vs. male-dominated) occupations. Our computational algorithm
auditing study demonstrates more pronounced representational and presentational
biases in DALL-E 2 compared to Google Images and calls for feminist
interventions to prevent such bias-laden AI-generated images to feedback into
the media ecology.