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

François Bachoc, Jérôme Bolte, Ryan Boustany and Jean-Michel Loubes

No 25-1641, TSE Working Papers from Toulouse School of Economics (TSE)

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.

Date: 2025-05-21
New Economics Papers: this item is included in nep-big and nep-cmp
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