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.

翻译：像DALL-E 2这样的生成式人工智能模型能够解读文本提示并生成展现人类创造力的高质量图像。尽管公众热情高涨，但对AI生成图像中潜在性别偏见的系统性审计仍然匮乏。我们通过检测153个职业中15300张DALL-E 2图像中两类职业性别偏见（表征偏见与呈现偏见）的普遍性来弥补这一空白，并以2021年人口普查劳动统计数据和谷歌图片为基准评估偏见的潜在放大效应。研究发现，DALL-E 2在男性主导领域对女性呈现不足，而在女性主导职业中则过度呈现。此外，与男性相比，DALL-E 2图像更倾向于描绘更多面带微笑、头部低垂的女性，尤其在女性主导（相较于男性主导）职业中更为显著。我们的计算算法审计研究表明，与谷歌图片相比，DALL-E 2的表征偏见和呈现偏见更为突出，并呼吁采取女性主义干预措施，防止此类充满偏见的AI生成图像反哺媒体生态。