用新指标评估并优化文生图模型对多元群体的代表性。
Multi-Group Proportional Representation for Text-to-Image Models
- 提出多群体比例代表度（MPR）度量方法，量化生成图像中群体代表性偏差。
- 实验显示，基于MPR优化后，模型在多个交叉群体间生成更均衡，且保持图像质量。
- 适合关注公平性、偏见控制的AI研究者与开发者使用。
文生图（T2I）生成模型能根据文本描述生成逼真图像。随着这类模型普及，其在多元群体表征、刻板印象传播和少数群体遮蔽方面引发新担忧。尽管人工智能安全与责任设计受到关注，但尚无系统性方法来衡量和控制图像生成中的表征伤害。本文提出一种新框架，通过多群体比例代表度（MPR）度量来评估T2I模型生成图像中交叉群体的代表性。MPR衡量生成图像中各群体代表性统计的最坏情况偏差，支持根据用户需求灵活定制。我们还开发了优化算法以使T2I模型适应该度量。实验表明，MPR能有效测量多个交叉群体的表征统计，当作为训练目标时，可引导模型在保持生成质量的前提下实现更均衡的群体生成。
原文摘要 · Abstract (English)
Text-to-image (T2I) generative models can create vivid, realistic images from textual descriptions. As these models proliferate, they expose new concerns about their ability to represent diverse demographic groups, propagate stereotypes, and efface minority populations. Despite growing attention to the "safe" and "responsible" design of artificial intelligence (AI), there is no established methodology to systematically measure and control representational harms in image generation. This paper introduces a novel framework to measure the representation of intersectional groups in images generated by T2I models by applying the Multi-Group Proportional Representation (MPR) metric. MPR evaluates the worst-case deviation of representation statistics across given population groups in images produced by a generative model, allowing for flexible and context-specific measurements based on user requirements. We also develop an algorithm to optimize T2I models for this metric. Through experiments, we demonstrate that MPR can effectively measure representation statistics across multiple intersectional groups and, when used as a training objective, can guide models toward a more balanced generation across demographic groups while maintaining generation quality.
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