[2605.06891] Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
[Submitted on 7 May 2026]
Title:Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
View a PDF of the paper titled Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation, by Aditya Parikh and 3 other authors
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Abstract:Labeled datasets reflect the biases of their annotation pipelines, which sometimes introduce label bias: group-conditional label errors that cause systematic performance disparities across demographic subgroups. Label bias in image segmentation remains underexplored, as even detecting it typically requires clean, unbiased annotations, which are not readily available. We present a data-centric adaptation of Confident Learning to segmentation, allowing detection of label bias directly in the training data without a clean, unbiased ground truth. By comparing the provided training labels to the model's confident predictions, we isolate directional errors that quantify the presence and nature of bias, where standard overlap metrics like Dice fail. We further show that label bias influences subgroup separability in the encoder's feature space, an artifact we leverage for bias mitigation rather than suppressing it. We evaluate three datasets, spanning from synthetic to real-life bias, showing how our framework reliably detects and mitigates bias without access to clean labels, achieving equitable performance across experimental conditions.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2605.06891 [cs.CV]
(or arXiv:2605.06891v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.06891
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Aditya Parikh [view email] [v1] Thu, 7 May 2026 19:47:50 UTC (14,681 KB)
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