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Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey

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arxiv 2207.07068 v4 pith:N52UWQFV submitted 2022-07-14 cs.LG

Max Hort , Zhenpeng Chen , Jie M. Zhang , Mark Harman , Federica Sarro This is my paper · ORCID
classification cs.LG
keywords biasmitigationmethodsclassifierscomprehensivedatasetsevaluatingfairness
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This paper provides a comprehensive survey of bias mitigation methods for achieving fairness in Machine Learning (ML) models. We collect a total of 341 publications concerning bias mitigation for ML classifiers. These methods can be distinguished based on their intervention procedure (i.e., pre-processing, in-processing, post-processing) and the technique they apply. We investigate how existing bias mitigation methods are evaluated in the literature. In particular, we consider datasets, metrics and benchmarking. Based on the gathered insights (e.g., What is the most popular fairness metric? How many datasets are used for evaluating bias mitigation methods?), we hope to support practitioners in making informed choices when developing and evaluating new bias mitigation methods.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 24 citations worldwide. Full citation record

  1. FairSelect: A Systematic Evaluation of Multi-Level and Intersectional Algorithmic Fairness

    cs.LG 2026-07 conditional novelty 5.0 of 10

    FairSelect systematically evaluates single and multi-level fairness interventions with intersectional metrics, finding non-additive, context-dependent effects on synthetic bias tests and a real AF stroke-risk task.

  2. U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detection

    cs.LG 2025-01 conditional novelty 5.0 of 10

    U-Fair, a gender-specific uncertainty reweighting for multitask depression detection, improves fairness over an uncertainty baseline but not consistently over unitask or vanilla multitask, and the PHQ-8 difficulty lin...

  3. BiasGuard: Guardrailing Fairness in Machine Learning Production Systems

    cs.LG 2025-01 conditional novelty 5.0 of 10

    BiasGuard uses test-time augmentation with CTGAN to flip protected attributes and average predictions, reporting a 31% average reduction in Equalized Odds with a 0.09% accuracy drop on five tabular benchmarks.

  4. Engineering Digital Systems for Humanity: a Research Roadmap

    cs.SE 2024-12 accept novelty 4.0 of 10

    A software engineering research roadmap organized around four challenges derived from humans' proactive, reactive and passive roles with digital systems, plus trust and trustworthiness.

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