Recent studies have highlighted biases in generative models, shedding light
on their predisposition towards gender-based stereotypes and imbalances. This
paper contributes to this growing body of research by introducing an evaluation
protocol designed to automatically analyze the impact of gender indicators on
Stable Diffusion images. Leveraging insights from prior work, we explore how
gender indicators not only affect gender presentation but also the
representation of objects and layouts within the generated images. Our findings
include the existence of differences in the depiction of objects, such as
instruments tailored for specific genders, and shifts in overall layouts. We
also reveal that neutral prompts tend to produce images more aligned with
masculine prompts than their feminine counterparts, providing valuable insights
into the nuanced gender biases inherent in Stable Diffusion.

该研究通过引入一个评估协议，旨在自动分析性别指示对稳定扩散图像的影响，从而为生成模型中的偏见和性别失衡问题做出贡献。研究发现性别指示不仅影响性别呈现，还影响生成图像中的物体和布局的表现，揭示出稳定扩散中微妙的性别偏见。

稳定的暴露扩散：从提示到图像的性别偏见

Stable Diffusion Exposed: Gender Bias from Prompt to Image

We present a large-scale study of gender bias in occupation classification, a
task where the use of machine learning may lead to negative outcomes on
peoples' lives. We analyze the potential allocation harms that can result from
semantic representation bias. To do so, we study the impact on occupation
classification of including explicit gender indicators---such as first names
and pronouns---in different semantic representations of online biographies.
Additionally, we quantify the bias that remains when these indicators are
"scrubbed," and describe proxy behavior that occurs in the absence of explicit
gender indicators. As we demonstrate, differences in true positive rates
between genders are correlated with existing gender imbalances in occupations,
which may compound these imbalances.

本研究就机器学习在职业分类中潜在的性别歧视问题进行了大规模调研，分析了不同语义表征中包含显性性别表示（如名字和代词）对职业分类的影响，并量化了这些表示被 “擦除” 后残留的偏见，并描述了在显性性别表示缺失时出现的代理行为。同时，研究也表明，不同性别的真正正率差异与职业领域中现存的性别不平衡存在相关性，这可能加剧这些不平衡。