mlsanityData to 7 Oct 2026

Fairness in Machine Learning: A Survey

arXiv 2010.04053 ↗First posted 4 Oct 2020cs.LG stat.MLcited by 369

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Abstract (arXiv)

As Machine Learning technologies become increasingly used in contexts that affect citizens, companies as well as researchers need to be confident that their application of these methods will not have unexpected social implications, such as bias towards gender, ethnicity, and/or people with disabilities. There is significant literature on approaches to mitigate bias and promote fairness, yet the area is complex and hard to penetrate for newcomers to the domain. This article seeks to provide an overview of the different schools of thought and approaches to mitigating (social) biases and increase fairness in the Machine Learning literature. It organises approaches into the widely accepted framework of pre-processing, in-processing, and post-processing methods, subcategorizing into a further 11 method areas. Although much of the literature emphasizes binary classification, a discussion of fairness in regression, recommender systems, unsupervised learning, and natural language processing is also provided along with a selection of currently available open source libraries. The article concludes by summarising open challenges articulated as four dilemmas for fairness research.

Key facts. Cited by 369 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Oct 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 23; 2022: 76; 2023: 79; 2024: 83; 2025: 76; 2026: 32.

369 papers.

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  1. 2021Trustworthy AI: From Principles to Practicescited by 206
  2. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  3. 2022AUC Maximization in the Era of Big Data and AI: A Surveycited by 100
  4. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  5. 2021Trustworthy AI: A Computational Perspectivecited by 93
  6. 2022Fairness in Graph Mining: A Surveycited by 76
  7. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  8. 2023Discovering and Mitigating Visual Biases through Keyword Explanationcited by 58
  9. 2022Fair and Optimal Classification via Post-Processingcited by 50
  10. 2024FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Modelscited by 47
  11. 2022Pruning has a disparate impact on model accuracycited by 43
  12. 2021Evaluating Robustness of Counterfactual Explanationscited by 41
38 more
  1. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  2. 2023A Survey on Intersectional Fairness in Machine Learning: Notions, Mitigation, and Challengescited by 41
  3. 2023Improving Fairness in Deepfake Detectioncited by 40
  4. 2022Gender Artifacts in Visual Datasetscited by 38
  5. 2022Representation Bias in Data: A Survey on Identification and Resolution Techniquescited by 37
  6. 2022Going Beyond XAI: A Systematic Survey for Explanation-Guided Learningcited by 35
  7. 2021How to avoid machine learning pitfalls: a guide for academic researcherscited by 34
  8. 2022Fairness Reprogrammingcited by 33
  9. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  10. 2022Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectivescited by 32
  11. 2021Measuring Fairness in Generative Modelscited by 31
  12. 2021Interpretable Data-Based Explanations for Fairness Debuggingcited by 30
  13. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  14. 2022CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systemscited by 29
  15. 2024FairGT: A Fairness-aware Graph Transformercited by 29
  16. 2021Strong Optimal Classification Treescited by 27
  17. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  18. 2023On The Impact of Machine Learning Randomness on Group Fairnesscited by 26
  19. 2023Group Robust Classification Without Any Group Informationcited by 25
  20. 2021Adaptive Sampling for Minimax Fair Classificationcited by 24
  21. 2022Bayes-Optimal Classifiers under Group Fairnesscited by 24
  22. 2022Rethinking Fairness: An Interdisciplinary Survey of Critiques of Hegemonic ML Fairness Approachescited by 23
  23. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
  24. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  25. 2022Transferring Fairness under Distribution Shifts via Fair Consistency Regularizationcited by 21
  26. 2024Towards Trustworthy AI: A Review of Ethical and Robust Large Language Modelscited by 21
  27. 2022FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neuronscited by 20
  28. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  29. 2022Challenges in Applying Explainability Methods to Improve the Fairness of NLP Modelscited by 20
  30. 2023Re-weighting Based Group Fairness Regularization via Classwise Robust Optimizationcited by 20
  31. 2023Bias and Fairness in Chatbots: An Overviewcited by 20
  32. 2022"There Is Not Enough Information": On the Effects of Explanations on Perceptions of Informational Fairness and Trustworthiness in Automated Decision-Makingcited by 19
  33. 2022Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiencycited by 19
  34. 2023Fairness of ChatGPTcited by 19
  35. 2022Achieving Long-Term Fairness in Sequential Decision Makingcited by 18
  36. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  37. 2022Differential Privacy has Bounded Impact on Fairness in Classificationcited by 18
  38. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18

50 most cited of 369 All 369 (JSON)