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arXiv · 2505.13122

When majority rules, minority loses: bias amplification of gradient descent

François Bachoc·Jérôme Bolte·Ryan Boustany·Jean-Michel Loubes
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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.

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François Bachoc, Jérôme Bolte, Ryan Boustany, Jean-Michel Loubes. 2026-09-15. When majority rules, minority loses: bias amplification of gradient descent. https://arxiv.org/abs/2505.13122

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