SOMtime the World Ain$'$t Fair: Violating Fairness Using Self-Organizing Maps

arXiv:2602.18201v2 Announce Type: replace-cross Abstract: Unsupervised representations are widely assumed to be neutral with respect to sensitive attributes when those attributes are withheld from training. We show that this assumption is false. Using SOMtime, a topology-preserving representation method based on high-capacity Self-Organizing Maps, we demonstrate that sensitive attributes such as age and income emerge as dominant latent axes in purely unsupervised embeddings, even when explicitly excluded from the input. On two large-scale real-world datasets (the World Values Survey across fiv

Source: arXiv cs.LG — read the full report at the original publisher.

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