Smiling Women Pitching Down: Auditing Representational and Presentational Gender Biases in Image Generative AI

• URL: http://arxiv.org/abs/2305.10566v1
• Date: Wed, 17 May 2023 20:59:10 GMT
• Title: Smiling Women Pitching Down: Auditing Representational and Presentational Gender Biases in Image Generative AI
• Authors: Luhang Sun, Mian Wei, Yibing Sun, Yoo Ji Suh, Liwei Shen, Sijia Yang
• Abstract summary: We examined the prevalence of two occupational gender biases in 15,300 DALL-E 2 images spanning 153 occupations. DALL-E 2 underrepresents women in male-dominated fields while overrepresenting them in female-dominated occupations. Our computational algorithm auditing study demonstrates more pronounced representational and presentational biases in DALL-E 2 compared to Google Images.
• Score: 0.6990493129893111
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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