Abstract
Despite growing empirical evidence of bias amplification in machine learning, its theoretical foundations remain poorly understood. We develop a formal framework for majority-minority learning tasks, showing how standard training can favor majority groups and produce stereotypical predictors that neglect minority-specific features. Assuming population and variance imbalance, our analysis reveals three key findings: (i) the close proximity between ``full-data'' and stereotypical predictors, (ii) the dominance of a region where training the entire model tends to merely learn the majority traits, and (iii) a lower bound on the additional training required. Our results are illustrated through experiments in deep learning for tabular and image classification tasks.
Abstract (translated)
尽管有关机器学习中偏见放大的实证证据越来越多，但其理论基础仍然不甚明了。我们建立了一个用于多数群体和少数群体学习任务的形式框架，展示了标准训练如何倾向于支持多数群体，并产生忽视少数群体特定特征的刻板印象预测模型。假设存在人口不平衡和方差差异的情况下，我们的分析揭示了三个关键发现：(i) “全数据”与刻板印象预测之间的紧密关联；(ii) 训练整个模型往往只是学习多数群体特性的主导区域；以及 (iii) 额外训练所需的最低限度。通过深度学习在表格和图像分类任务中的实验，我们展示了这些结果。
URL
https://arxiv.org/abs/2505.13122
