With the increasing use of image generation technology, understanding its social biases, including gender bias, is essential. This paper presents the first large-scale study on gender bias in text-to-image (T2I) models, focusing on everyday situations. While previous research has examined biases in occupations, we extend this analysis to gender associations in daily activities, objects, and contexts. We create a dataset of 3,217 gender-neutral prompts and generate 200 images per prompt from five leading T2I models. We automatically detect the perceived gender of people in the generated images and filter out images with no person or multiple people of different genders, leaving 2,293,295 images. To enable a broad analysis of gender bias in T2I models, we group prompts into semantically similar concepts and calculate the proportion of male- and female-gendered images for each prompt. Our analysis shows that T2I models reinforce traditional gender roles, reflect common gender stereotypes in household roles, and underrepresent women in financial related activities. Women are predominantly portrayed in care- and human-centered scenarios, and men in technical or physical labor scenarios.

翻译：随着图像生成技术的日益普及，理解其社会偏见（包括性别偏见）至关重要。本文首次针对文本到图像（T2I）模型中的性别偏见展开大规模研究，重点关注日常生活场景。以往研究主要考察职业领域的偏见，而我们将分析拓展至日常活动、物品及场景中的性别关联。我们构建了一个包含3,217个性别中立提示词的数据集，并使用五个主流T2I模型为每个提示生成200张图像。通过自动检测生成图像中人物的感知性别，并过滤掉无人像或包含多性别人像的图像，最终获得2,293,295张有效图像。为全面分析T2I模型中的性别偏见，我们将提示词按语义相似性分组，并计算每个提示对应图像中男性和女性形象的占比。分析表明：T2I模型强化了传统性别角色，反映了家庭分工中常见的性别刻板印象，并在金融相关活动中低估女性形象。女性主要被描绘在照护类和以人为中心的场景中，而男性则多出现在技术或体力劳动场景。