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Conditional Learning of Fair Representations

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arxiv 1910.07162 v3 pith:557LU2R6 submitted 2019-10-16 cs.LG cs.AIstat.ML

Han Zhao , Amanda Coston , Tameem Adel , Geoffrey J. Gordon This is my paper · ORCID
classification cs.LGcs.AIstat.ML
keywords representationsalgorithmfairlearningbalancedclassificationcomponentsconditional
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We propose a novel algorithm for learning fair representations that can simultaneously mitigate two notions of disparity among different demographic subgroups in the classification setting. Two key components underpinning the design of our algorithm are balanced error rate and conditional alignment of representations. We show how these two components contribute to ensuring accuracy parity and equalized false-positive and false-negative rates across groups without impacting demographic parity. Furthermore, we also demonstrate both in theory and on two real-world experiments that the proposed algorithm leads to a better utility-fairness trade-off on balanced datasets compared with existing algorithms on learning fair representations for classification.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Learning Representational Disparities

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A new fair-ML method learns interpretable differences between observed and desired human decisions to reduce downstream outcome disparity, with a proof of full mitigation under simplifying assumptions.

  2. FairDD: Enhancing Fairness with domain-incremental learning in dermatological disease diagnosis

    cs.LG 2024-12 conditional novelty 5.0 of 10

    FairDD uses domain-incremental replay, mixup, contrastive learning, and distillation to improve the accuracy-fairness tradeoff of dermatological classifiers on Fitzpatrick-17k and ISIC 2019.

  3. Fair Distillation: Teaching Fairness from Biased Teachers in Medical Imaging

    cs.CV 2024-11 conditional novelty 5.0 of 10

    FairDi trains biased per-group teachers and distills them into one student, reporting better accuracy-fairness trade-offs than prior methods on medical imaging benchmarks.

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