分析主流文生图模型中的性别偏见，发现其强化传统性别角色。
A Large Scale Analysis of Gender Biases in Text-to-Image Generative Models
- 构建3217个中性提示词，生成超200万张图像进行大规模分析
- 女性多被生成在照料类场景，男性多在技术或体力劳动场景
- 揭示模型对日常活动的性别刻板印象，适合关注AI伦理的研究者
随着图像生成技术广泛应用，理解其社会偏见（包括性别偏见）至关重要。本文对文本到图像（T2I）模型中的性别偏见进行了大规模研究，聚焦日常生活情境。不同于以往对职业偏见的关注，本研究扩展至日常活动、物品与场景的性别关联。我们创建包含3,217个性别中性提示词的数据集，从五种领先T2I模型中每提示生成5个变体，共生成200万张图像。通过自动检测生成图像中人物的感知性别，并剔除无人或多人不同性别图像，最终保留2,293,295张图像。为实现广泛分析，我们将提示词按语义分组，计算每组中男性与女性形象的比例。结果显示，T2I模型强化了传统性别角色，反映常见的性别刻板印象：女性主要出现在照护和以人为中心的场景，男性则多出现在技术或体力劳动场景。
原文摘要 · Abstract (English)
With the increasing use of image generation technology, understanding its social biases, including gender bias, is essential. This paper presents a 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 over 5 prompt variations 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 and reflect common gender stereotypes in household roles. Women are predominantly portrayed in care and human-centered scenarios, and men in technical or physical labor scenarios.
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