Abstract
Fairness is a crucial concern for generative models, which not only reflect but can also amplify societal and cultural biases. Existing fairness notions for generative models are largely adapted from classification and focus on balancing the probability of generating samples from each sensitive group. We show that such criteria are brittle, as they can be met even when different sensitive groups are modeled with widely varying quality. To address this limitation, we introduce a new fairness definition for generative models, termed as equalized generative treatment (EGT), which requires comparable generation quality across all sensitive groups, with quality measured via a reference f-divergence. We further analyze the trade-offs induced by EGT, demonstrating that enforcing fairness constraints necessarily couples the overall model quality to that of the most challenging group to approximate. This indicates that a simple yet efficient min-max fine-tuning method should be able to balance f-divergences across sensitive groups to satisfy EGT. We validate this theoretical insight through a set of experiments on both image and text generation tasks. We demonstrate that min-max methods consistently achieve fairer outcomes compared to other approaches from the literature, while maintaining competitive overall performance for both tasks.
Abstract (translated)
公平性是生成模型中的一个关键问题，因为这些模型不仅会反映社会和文化的偏见，还可能放大这些问题。现有的针对生成模型的公平概念大多借鉴自分类方法，并主要集中在平衡从每个敏感群体中生成样本的概率上。我们证明了这种标准较为脆弱，即使当不同敏感群体的质量相差甚远时，也仍然可以满足这些标准。 为了解决这一局限性，我们引入了一种新的用于生成模型的公平定义——等效生成待遇（Equalized Generative Treatment, EGT），它要求所有敏感群体之间具有可比较的生成质量，并通过参考 f-散度来测量这种质量。进一步地，我们分析了由 EGT 引起的权衡关系，表明实施公平性约束将不可避免地使整体模型质量与最难以接近近似的困难群体的质量相挂钩。这表明了一种简单的最小化—最大化微调方法应该能够平衡敏感群体间的 f-散度以满足 EGT 要求。 我们通过一系列针对图像和文本生成任务的实验验证了这一理论见解，并展示了使用最小—最大方法可以持续实现比其他文献中的方法更加公平的结果，同时保持在两项任务上的竞争力。
URL
https://arxiv.org/abs/2602.08660
