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arXiv AI By Fran\c{c}ois Bachoc (LPP), J\'er\^ome Bolte (TSE-R), Ryan Boustany (TSE-R), Jean-Michel Loubes (IMT, REGALIA)

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

Read the original on arXiv AI →

The paper presents a formal framework for majority‑minority learning tasks, demonstrating that standard gradient‑descent training can amplify bias by favoring majority groups and producing stereotypical predictors that ignore minority‑specific features. It identifies three key theoretical findings: the close proximity between full‑data and stereotypical predictors, the dominance of a region where training the entire model mainly captures majority traits, and a lower bound on the extra training needed to mitigate this effect. Experiments on deep learning for tabular and image classification illustrate these results.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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