Smiling women pitching down: auditing representational
and presentational gender biases in image-generative AI

Smiling women pitching down: auditing representational
and presentational gender biases in image-generative AI

Generative AI, DALLE 2, gender bias, algorithm auditing, computer vision.

Generative Artificial Intelligence (AI) models like DALLE 2 can interpret prompts and generate high-quality images that exhibit 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 DALLE 2 images
spanning 153 occupations. We assessed potential bias amplification by benchmarking against the 2021 U.S. census data and Google
Images. Our findings reveal that DALLE 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated
occupations. Additionally, DALLE 2 images tend to depict more women than men with smiles and downward-pitching heads, particularly in

female-dominated (versus male-dominated) occupations. Our algorithm auditing study demonstrates more pronounced representational and pre-
sentational biases in DALLE 2 compared to Google Images and calls for feminist interventions to curtail the potential impacts of such biased AI-
generated images on the media ecology.

Luhang Sun 1

, Mian Wei 1

, Yibing Sun 1

, Yoo Ji Suh 1

, Liwei Shen 2

, Sijia Yang

https://doi.org/10.1093/jcmc/zmad045

Oxford University Press on behalf of International Communication Association.

4 October 2023

PERI IRAWAN

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