mlsanityData to 7 Oct 2026

Preserving AUC Fairness in Learning with Noisy Protected Groups

arXiv 2505.18532 ↗First posted 24 May 2025cs.LGcited by 6

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Abstract (arXiv)

The Area Under the ROC Curve (AUC) is a key metric for classification, especially under class imbalance, with growing research focus on optimizing AUC over accuracy in applications like medical image analysis and deepfake detection. This leads to fairness in AUC optimization becoming crucial as biases can impact protected groups. While various fairness mitigation techniques exist, fairness considerations in AUC optimization remain in their early stages, with most research focusing on improving AUC fairness under the assumption of clean protected groups. However, these studies often overlook the impact of noisy protected groups, leading to fairness violations in practice. To address this, we propose the first robust AUC fairness approach under noisy protected groups with fairness theoretical guarantees using distributionally robust optimization. Extensive experiments on tabular and image datasets show that our method outperforms state-of-the-art approaches in preserving AUC fairness. The code is in https://github.com/Purdue-M2/AUC_Fairness_with_Noisy_Groups.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 37 of its 57 references that are in the map. First posted on arXiv 24 May 2025.

Builds on

37 of 57 references.

References · 37

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25 more
  1. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  2. 2020Large-Scale Methods for Distributionally Robust Optimizationcited by 179
  3. 2019Fairness in Recommendation Ranking through Pairwise Comparisonscited by 127
  4. 2019Fairness risk measurescited by 85
  5. 2020Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classificationcited by 84
  6. 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
  7. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
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  9. 2024Preserving Fairness Generalization in Deepfake Detectioncited by 70
  10. 2019Pairwise Fairness for Ranking and Regressioncited by 58
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  12. 2023Improving Fairness in Deepfake Detectioncited by 40
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  19. 2022Fair Ranking with Noisy Protected Attributescited by 17
  20. 2022Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecited by 16
  21. 2024Vision-Language Models are Strong Noisy Label Detectorscited by 16
  22. 2024Fairness in Survival Analysis with Distributionally Robust Optimizationcited by 13
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.

  1. 2025Rethinking Individual Fairness in Deepfake Detectioncited by 4
  2. 2025SynthGuard: An Open Platform for Detecting AI-Generated Multimedia with Multimodal LLMscited by 0
  3. 2025Are Detectors Fair to Indian IP-AIGC? A Cross-Generator Studycited by 0
  4. 2026Robust Multi-Source Covid-19 Detection in CT Imagescited by 0
  5. 2026Robust Fair Disease Diagnosis in CT Imagescited by 0
  6. 2026Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Studycited by 0