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Related Experiment Videos

Task-tailored Pre-processing: Fair Downstream Supervised Learning.

Jinwon Sohn, Qifan Song, Guang Lin

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |May 28, 2026
    PubMed
    Summary
    This summary is machine-generated.

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    We introduce a new pre-processing method for fair machine learning that improves fairness without sacrificing predictive accuracy. This task-tailored approach offers better trade-offs between fairness and utility compared to existing data fairness methods.

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Fairness-aware machine learning aims to mitigate discrimination in data-driven tasks.
    • Existing pre-processing methods fall into data fairness (group-independent) and task-tailored fairness (group-dependent).
    • Data fairness methods may impose overly strong regularization, impacting utility.

    Purpose of the Study:

    • To develop a novel pre-processing approach for supervised learning that is tailored to the specific task.
    • To theoretically investigate the trade-offs between fairness and utility.
    • To provide downstream guarantees for fairness improvement and utility preservation.

    Main Methods:

    • Devised a novel pre-processing approach explicitly considering the supervised learning task.

    Related Experiment Videos

  • Analyzed the trade-off between fairness and utility in the pre-processing map.
  • Studied downstream model behavior on transformed data to establish fairness and utility guarantees.
  • Evaluated the framework on tabular and image datasets.

    • Main Results:

    • The proposed task-tailored fairness approach outperforms existing methods in preserving fairness-utility trade-offs.
    • Demonstrated superiority on both tabular and image datasets.
    • Showed that the method alters only necessary semantic features for fairness, particularly in computer vision tasks.
    • Provided theoretical guarantees for downstream model fairness and utility.

    Conclusions:

    • The novel task-tailored pre-processing framework offers a superior approach to achieving algorithmic fairness in supervised learning.
    • The method effectively balances fairness and utility, outperforming existing techniques.
    • Theoretical guarantees and empirical results validate the framework's effectiveness across diverse datasets.