Recent large-scale Text-To-Image (T2I) models such as DALLE-3 demonstrate
great potential in new applications, but also face unprecedented fairness
challenges. Prior studies revealed gender biases in single-person image
generation, but T2I model applications might require portraying two or more
people simultaneously. Potential biases in this setting remain unexplored,
leading to fairness-related risks in usage. To study these underlying facets of
gender biases in T2I models, we propose a novel Paired Stereotype Test (PST)
bias evaluation framework. PST prompts the model to generate two individuals in
the same image. They are described with two social identities that are
stereotypically associated with the opposite gender. Biases can then be
measured by the level of conformation to gender stereotypes in generated
images. Using PST, we evaluate DALLE-3 from 2 perspectives: biases in gendered
occupation and biases in organizational power. Despite seemingly fair or even
anti-stereotype single-person generations, PST still unveils gendered
occupational and power associations. Moreover, compared to single-person
settings, DALLE-3 generates noticeably more masculine figures under PST for
individuals with male-stereotypical identities. PST is therefore effective in
revealing underlying gender biases in DALLE-3 that single-person settings
cannot capture. Our findings reveal the complicated patterns of gender biases
in modern T2I models, further highlighting the critical fairness challenges in
multimodal generative systems.

最近所提出的大规模图文生成模型（如 DALLE-3）在新应用中表现出很大的潜力，但也面临着前所未有的公平性挑战。此研究通过提出一种新颖的配对刻板印象测试（PST）偏见评估框架来研究这些 T2I 模型中性别偏见的潜在因素。通过 PST，我们从两个方面评估了 DALLE-3：性别职业偏见和组织权力偏见。结果表明，尽管 DALLE-3 在单人情景下似乎公平甚至与刻板印象相悖，但在 PST 下仍揭示了性别职业偏见和权力关联偏见。PST 有效地揭示了 DALLE-3 中单人情景无法捕捉到的潜在性别偏见，进一步凸显了多模态生成系统中的公平性挑战。

男性 CEO 和女性助理：通过成对定型测试探索文本到图像模型中的性别偏见

The Male CEO and the Female Assistant: Probing Gender Biases in  Text-To-Image Models Through Paired Stereotype Test

Liver transplantation often faces fairness challenges across subgroups
defined by sensitive attributes like age group, gender, and race/ethnicity.
Machine learning models for outcome prediction can introduce additional biases.
To address these, we introduce Fairness through the Equitable Rate of
Improvement in Multitask Learning (FERI) algorithm for fair predictions of
graft failure risk in liver transplant patients. FERI constrains subgroup loss
by balancing learning rates and preventing subgroup dominance in the training
process. Our experiments show that FERI maintains high predictive accuracy with
AUROC and AUPRC comparable to baseline models. More importantly, FERI
demonstrates an ability to improve fairness without sacrificing accuracy.
Specifically, for gender, FERI reduces the demographic parity disparity by
71.74%, and for the age group, it decreases the equalized odds disparity by
40.46%. Therefore, the FERI algorithm advances fairness-aware predictive
modeling in healthcare and provides an invaluable tool for equitable healthcare
systems.

通过 FERI 算法实现对肝移植患者移植物失败风险公平预测，维持高准确率并显著改善性别和年龄群体之间的公平问题，为公正的医疗系统提供了宝贵工具。