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.

本研究分析了 15,300 个 DALL-E 2 图像，发现其在男性主导领域中低估女性，而在女性主导领域中高估女性，同时揭示了这些图像中的性别偏见，需要采取女性主义干预手段以防止这些带有偏见的 AI 生成图片反馈到媒体生态环境中。

笑脸女性下降：审计图像生成 AI 中的代表性和呈现性性别偏见

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

Text-to-Image (T2I) generation is enabling new applications that support
creators, designers, and general end users of productivity software by
generating illustrative content with high photorealism starting from a given
descriptive text as a prompt. Such models are however trained on massive
amounts of web data, which surfaces the peril of potential harmful biases that
may leak in the generation process itself. In this paper, we take a
multi-dimensional approach to studying and quantifying common social biases as
reflected in the generated images, by focusing on how occupations, personality
traits, and everyday situations are depicted across representations of
(perceived) gender, age, race, and geographical location. Through an extensive
set of both automated and human evaluation experiments we present findings for
two popular T2I models: DALLE-v2 and Stable Diffusion. Our results reveal that
there exist severe occupational biases of neutral prompts majorly excluding
groups of people from results for both models. Such biases can get mitigated by
increasing the amount of specification in the prompt itself, although the
prompting mitigation will not address discrepancies in image quality or other
usages of the model or its representations in other scenarios. Further, we
observe personality traits being associated with only a limited set of people
at the intersection of race, gender, and age. Finally, an analysis of
geographical location representations on everyday situations (e.g., park, food,
weddings) shows that for most situations, images generated through default
location-neutral prompts are closer and more similar to images generated for
locations of United States and Germany.

本文通过对两个流行的 T2I 模型（DALLE-v2 和 Stable Diffusion）进行广泛的自动化和人工评估实验，专注于反映出的性别、年龄、种族和地理位置之间的职业，人格特征和日常情况的生成图像，研究和量化常见的社会偏见。我们的研究结果表明，这些模型中存在严重的职业偏见和地理位置代表的日常情况。尽管可以通过增加提示本身的详细信息来缓解这些偏差，但提示缓解可能无法解决图像质量或模型在其他场景中的其他用途的差异。