mlsanityData to 7 Oct 2026

Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey

arXiv 2207.07068 ↗First posted 14 Jul 2022cs.LGcited by 125

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Abstract (arXiv)

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.

Key facts. Cited by 125 papers in the map (data through 7 Oct 2026). Builds on 229 of its 428 references that are in the map. First posted on arXiv 14 Jul 2022.

Builds on

229 of 428 references.

References · 229

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2020Language (Technology) is Power: A Critical Survey of "Bias" in NLPcited by 951
  4. 2017Counterfactual Fairnesscited by 802
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2018A Reductions Approach to Fair Classificationcited by 585
  8. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  9. 2017On Fairness and Calibrationcited by 406
  10. 2017Algorithmic decision making and the cost of fairnesscited by 402
  11. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  12. 2020Fairness in Machine Learning: A Surveycited by 369
38 more
  1. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  2. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  3. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  4. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  5. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  6. 2019Mitigating Gender Bias in Natural Language Processing: Literature Reviewcited by 287
  7. 2018Improving fairness in machine learning systems: What do industry practitioners need?cited by 262
  8. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  9. 2017Predict Responsibly: Improving Fairness and Accuracy by Learning to Defercited by 228
  10. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  11. 2015The Variational Fair Autoencodercited by 223
  12. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 221
  13. 2019Machine Learning Testing: Survey, Landscapes and Horizonscited by 213
  14. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  15. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
  16. 2015Censoring Representations with an Adversarycited by 191
  17. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  18. 2018Path-Specific Counterfactual Fairnesscited by 179
  19. 2018Learning Gender-Neutral Word Embeddingscited by 169
  20. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  21. 2016Fairness in Learning: Classic and Contextual Banditscited by 167
  22. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  23. 2018FairGAN: Fairness-aware Generative Adversarial Networkscited by 164
  24. 2021A survey on datasets for fairness-aware machine learningcited by 157
  25. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 152
  26. 2017A Convex Framework for Fair Regressioncited by 147
  27. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  28. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  29. 2019Inherent Tradeoffs in Learning Fair Representationscited by 144
  30. 2017Fair Inference On Outcomescited by 143
  31. 2018Why Is My Classifier Discriminatory?cited by 143
  32. 2020Algorithmic Fairnesscited by 143
  33. 2018The Frontiers of Fairness in Machine Learningcited by 139
  34. 2019Wasserstein Fair Classificationcited by 131
  35. 2017Controllable Invariance through Adversarial Feature Learningcited by 127
  36. 2021FairFed: Enabling Group Fairness in Federated Learningcited by 114
  37. 2018Invariant Representations without Adversarial Trainingcited by 111
  38. 2021Fair Mixup: Fairness via Interpolationcited by 109

50 most cited of 229 All 229 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 32; 2024: 35; 2025: 39; 2026: 18.

125 papers.

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  1. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  2. 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
  3. 2024Fairness and Bias in Multimodal AI: A Surveycited by 19
  4. 2025Systemizing Multiplicity: The Curious Case of Arbitrariness in Machine Learningcited by 17
  5. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  6. 2023Uncertainty-based Fairness Measurescited by 15
  7. 2024New Job, New Gender? Measuring the Social Bias in Image Generation Modelscited by 15
  8. 2025U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detectioncited by 12
  9. 2023Post-processing Private Synthetic Data for Improving Utility on Selected Measurescited by 9
  10. 2023Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoMLcited by 9
  11. 2023Understanding Fairness Surrogate Functions in Algorithmic Fairnesscited by 8
  12. 2023Fair Wasserstein Coresetscited by 8
38 more
  1. 2023Bias Behind the Wheel: Fairness Testing of Autonomous Driving Systemscited by 7
  2. 2023Learning Human-like Representations to Enable Learning Human Valuescited by 7
  3. 2025Machine Learning Fairness for Depression Detection using EEG Datacited by 7
  4. 2025Review of Demographic Fairness in Face Recognitioncited by 7
  5. 2023Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learningcited by 6
  6. 2024Underneath the Numbers: Quantitative and Qualitative Gender Fairness in LLMs for Depression Predictioncited by 6
  7. 2024FairJob: A Real-World Dataset for Fairness in Online Systemscited by 6
  8. 2023Compatibility of Fairness Metrics with EU Non-Discrimination Laws: Demographic Parity & Conditional Demographic Disparitycited by 5
  9. 2023Arbitrariness Lies Beyond the Fairness-Accuracy Frontiercited by 5
  10. 2023FairWASP: Fast and Optimal Fair Wasserstein Pre-processingcited by 5
  11. 2023DSAP: Analyzing Bias Through Demographic Comparison of Datasetscited by 5
  12. 2024Implications of the AI Act for Non-Discrimination Law and Algorithmic Fairnesscited by 5
  13. 2024What is Fair? Defining Fairness in Machine Learning for Healthcited by 5
  14. 2024Less can be more: representational vs. stereotypical gender bias in facial expression recognitioncited by 5
  15. 2024EARN Fairness: Explaining, Asking, Reviewing, and Negotiating Artificial Intelligence Fairness Metrics Among Stakeholderscited by 5
  16. 2024Fairness Concerns in App Reviews: A Study on AI-based Mobile Appscited by 4
  17. 2024A Catalog of Fairness-Aware Practices in Machine Learning Engineeringcited by 4
  18. 2025Self-Regularization with Sparse Autoencoders for Controllable LLM-based Classificationcited by 4
  19. 2025BIASINSPECTOR: Detecting Bias in Structured Data through LLM Agentscited by 4
  20. 2023Standardized Interpretable Fairness Measures for Continuous Risk Scorescited by 3
  21. 2023RAI4IoE: Responsible AI for Enabling the Internet of Energycited by 3
  22. 2023Enhancing Group Fairness in Online Settings Using Oblique Decision Forestscited by 3
  23. 2023Multi-Group Fairness Evaluation via Conditional Value-at-Risk Testingcited by 3
  24. 2024Inducing Group Fairness in Prompt-Based Language Model Decisionscited by 3
  25. 2024AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Biascited by 3
  26. 2024Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics Statementscited by 3
  27. 2024Diversity Drives Fairness: Ensemble of Higher Order Mutants for Intersectional Fairness of Machine Learning Softwarecited by 3
  28. 2025Fair Text Classification via Transferable Representationscited by 3
  29. 2025Counterfactual Fairness Evaluation of Machine Learning Models on Educational Datasetscited by 3
  30. 2025Generative AI for Testing of Autonomous Driving Systems: A Surveycited by 3
  31. 2022Enhanced Fairness Testing via Generating Effective Initial Individual Discriminatory Instancescited by 2
  32. 2023Pushing the Accuracy-Group Robustness Frontier with Introspective Self-playcited by 2
  33. 2023Fair Active Learning in Low-Data Regimescited by 2
  34. 2025Deontic Temporal Logic for Formal Verification of AI Ethicscited by 2
  35. 2025Underrepresentation, Label Bias, and Proxies: Towards Data Bias Profiles for the EU AI Act and Beyondcited by 2
  36. 2025Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisationcited by 2
  37. 2026The Voice of Equity: A Systematic Evaluation of Bias Mitigation Techniques for Speech-Based Cognitive Impairment Detection Across Architectures and Demographicscited by 2
  38. 2026FairLogue: A Toolkit for Intersectional Fairness Analysis in Clinical Machine Learning Modelscited by 2

50 most cited of 125 All 125 (JSON)