Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation

• URL: http://arxiv.org/abs/2605.06891v1
• Date: Thu, 07 May 2026 19:47:50 GMT
• Title: Towards Fairness under Label Bias in Image Segmentation: Impact, Measurement and Mitigation
• Abstract summary: Labeled datasets reflect the biases of their annotation pipelines, which sometimes introduce label bias.<n>We present a data-centric adaptation of Confident Learning to segmentation, allowing detection of label bias directly in the training data.<n>We show how our framework reliably detects and mitigates bias without access to clean labels, achieving equitable performance across experimental conditions.
• Score: 10.15221228043609
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Bias Leaves a Gradient Trail: Label-Free Bias Identification via Gradient Probes on Concept Decompositions [8.894428198928333]
  We present a bias-label-free, post-hoc method for identifying spurious concepts in frozen vision models.<n>On Colored MNIST and Waterbirds the method recovers concepts aligned with the known spurious cue.<n> suppressing the top-ranked concepts at inference time improves worst-group accuracy by up to 17.9 percentage points.
  arXiv  Detail & Related papers  (2026-05-27T17:39:10Z)
• Detecting labeling bias using influence functions [1.9276091419970947]
  influence functions can be used to detect labeling bias.<n>We develop a sample valuation pipeline and test it first on the MNIST dataset.<n>On CheXpert, mislabeled samples consistently exhibit higher influence scores.
  arXiv  Detail & Related papers  (2026-02-22T11:20:35Z)
• Label Distribution Learning with Biased Annotations by Learning Multi-Label Representation [120.97262070068224]
  Multi-label learning (MLL) has gained attention for its ability to represent real-world data.<n>Label Distribution Learning (LDL) faces challenges in collecting accurate label distributions.
  arXiv  Detail & Related papers  (2025-02-03T09:04:03Z)
• Mitigating Label Bias in Machine Learning: Fairness through Confident Learning [22.031325797588476]
  Discrimination can occur when the underlying unbiased labels are overwritten by an agent with potential bias. In this paper, we demonstrate that it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning.
  arXiv  Detail & Related papers  (2023-12-14T08:55:38Z)
• On Comparing Fair Classifiers under Data Bias [42.43344286660331]
  We study the effect of varying data biases on the accuracy and fairness of fair classifiers. Our experiments show how to integrate a measure of data bias risk in the existing fairness dashboards for real-world deployments.
  arXiv  Detail & Related papers  (2023-02-12T13:04:46Z)
• Dist-PU: Positive-Unlabeled Learning from a Label Distribution Perspective [89.5370481649529]
  We propose a label distribution perspective for PU learning in this paper. Motivated by this, we propose to pursue the label distribution consistency between predicted and ground-truth label distributions. Experiments on three benchmark datasets validate the effectiveness of the proposed method.
  arXiv  Detail & Related papers  (2022-12-06T07:38:29Z)
• More Data Can Lead Us Astray: Active Data Acquisition in the Presence of Label Bias [7.506786114760462]
  Proposed bias mitigation strategies typically overlook the bias presented in the observed labels. We first present an overview of different types of label bias in the context of supervised learning systems. We then empirically show that, when overlooking label bias, collecting more data can aggravate bias, and imposing fairness constraints that rely on the observed labels in the data collection process may not address the problem.
  arXiv  Detail & Related papers  (2022-07-15T19:30:50Z)
• Debiased Pseudo Labeling in Self-Training [77.83549261035277]
  Deep neural networks achieve remarkable performances on a wide range of tasks with the aid of large-scale labeled datasets. To mitigate the requirement for labeled data, self-training is widely used in both academia and industry by pseudo labeling on readily-available unlabeled data. We propose Debiased, in which the generation and utilization of pseudo labels are decoupled by two independent heads.
  arXiv  Detail & Related papers  (2022-02-15T02:14:33Z)
• Balancing out Bias: Achieving Fairness Through Training Reweighting [58.201275105195485]
  Bias in natural language processing arises from models learning characteristics of the author such as gender and race. Existing methods for mitigating and measuring bias do not directly account for correlations between author demographics and linguistic variables. This paper introduces a very simple but highly effective method for countering bias using instance reweighting.
  arXiv  Detail & Related papers  (2021-09-16T23:40:28Z)
• Disentangling Sampling and Labeling Bias for Learning in Large-Output Spaces [64.23172847182109]
  We show that different negative sampling schemes implicitly trade-off performance on dominant versus rare labels. We provide a unified means to explicitly tackle both sampling bias, arising from working with a subset of all labels, and labeling bias, which is inherent to the data due to label imbalance.
  arXiv  Detail & Related papers  (2021-05-12T15:40:13Z)
• Debiased Contrastive Learning [64.98602526764599]
  We develop a debiased contrastive objective that corrects for the sampling of same-label datapoints. Empirically, the proposed objective consistently outperforms the state-of-the-art for representation learning in vision, language, and reinforcement learning benchmarks.
  arXiv  Detail & Related papers  (2020-07-01T04:25:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.