Fairness Without Labels: Pseudo-Balancing for Bias Mitigation in Face Gender Classification

• URL: http://arxiv.org/abs/2510.10191v1
• Date: Sat, 11 Oct 2025 12:08:40 GMT
• Title: Fairness Without Labels: Pseudo-Balancing for Bias Mitigation in Face Gender Classification
• Authors: Haohua Dong, Ana Manzano Rodríguez, Camille Guinaudeau, Shin'ichi Satoh,
• Abstract summary: Face gender classification models often reflect and amplify demographic biases present in their training data.<n>We introduce pseudo-balancing, a simple and effective strategy for mitigating such biases in semi-supervised learning.<n>Our method enforces demographic balance during pseudo-label selection, using only unlabeled images from a race-balanced dataset.
• Score: 10.66892435479991
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Face gender classification models often reflect and amplify demographic biases present in their training data, leading to uneven performance across gender and racial subgroups. We introduce pseudo-balancing, a simple and effective strategy for mitigating such biases in semi-supervised learning. Our method enforces demographic balance during pseudo-label selection, using only unlabeled images from a race-balanced dataset without requiring access to ground-truth annotations. We evaluate pseudo-balancing under two conditions: (1) fine-tuning a biased gender classifier using unlabeled images from the FairFace dataset, and (2) stress-testing the method with intentionally imbalanced training data to simulate controlled bias scenarios. In both cases, models are evaluated on the All-Age-Faces (AAF) benchmark, which contains a predominantly East Asian population. Our results show that pseudo-balancing consistently improves fairness while preserving or enhancing accuracy. The method achieves 79.81% overall accuracy - a 6.53% improvement over the baseline - and reduces the gender accuracy gap by 44.17%. In the East Asian subgroup, where baseline disparities exceeded 49%, the gap is narrowed to just 5.01%. These findings suggest that even in the absence of label supervision, access to a demographically balanced or moderately skewed unlabeled dataset can serve as a powerful resource for debiasing existing computer vision models.

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