mlsanityData to 7 Oct 2026

A survey on datasets for fairness-aware machine learning

arXiv 2110.00530 ↗First posted 1 Oct 2021cs.LGcited by 157

Not on the map. Why?

Abstract (arXiv)

As decision-making increasingly relies on Machine Learning (ML) and (big) data, the issue of fairness in data-driven Artificial Intelligence (AI) systems is receiving increasing attention from both research and industry. A large variety of fairness-aware machine learning solutions have been proposed which involve fairness-related interventions in the data, learning algorithms and/or model outputs. However, a vital part of proposing new approaches is evaluating them empirically on benchmark datasets that represent realistic and diverse settings. Therefore, in this paper, we overview real-world datasets used for fairness-aware machine learning. We focus on tabular data as the most common data representation for fairness-aware machine learning. We start our analysis by identifying relationships between the different attributes, particularly w.r.t. protected attributes and class attribute, using a Bayesian network. For a deeper understanding of bias in the datasets, we investigate the interesting relationships using exploratory analysis.

Key facts. Cited by 157 papers in the map (data through 7 Oct 2026). Builds on 63 of its 161 references that are in the map. First posted on arXiv 1 Oct 2021.

Builds on

63 of 161 references.

References · 63

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2017Counterfactual Fairnesscited by 802
  4. 2014Certifying and removing disparate impactcited by 715
  5. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  6. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  7. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  8. 2018A Reductions Approach to Fair Classificationcited by 585
  9. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 443
  10. 2017Algorithmic decision making and the cost of fairnesscited by 402
  11. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
  12. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
38 more
  1. 2020Data and its (dis)contents: A survey of dataset development and use in machine learning researchcited by 259
  2. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  3. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  4. 2018Delayed Impact of Fair Machine Learningcited by 183
  5. 2018Fair Clustering Through Fairletscited by 176
  6. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 173
  7. 2020Minimax Pareto Fairness: A Multi Objective Perspectivecited by 152
  8. 2017A Convex Framework for Fair Regressioncited by 147
  9. 2020Algorithmic Fairnesscited by 143
  10. 2019Fairness in Deep Learning: A Computational Perspectivecited by 123
  11. 2019Fair Algorithms for Clusteringcited by 98
  12. 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
  13. 2018An Empirical Study of Rich Subgroup Fairness for Machine Learningcited by 81
  14. 2019Scalable Fair Clusteringcited by 78
  15. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  16. 2020Learning Certified Individually Fair Representationscited by 69
  17. 2019Operationalizing Individual Fairness with Pairwise Fair Representationscited by 64
  18. 2019FAHT: An Adaptive Fairness-aware Decision Tree Classifiercited by 63
  19. 2021Fairness in Rankings and Recommendations: An Overviewcited by 62
  20. 2019FairSight: Visual Analytics for Fairness in Decision Makingcited by 59
  21. 2020How Do Fair Decisions Fare in Long-term Qualification?cited by 59
  22. 2019Pairwise Fairness for Ranking and Regressioncited by 58
  23. 2020Individual Fairness for k-Clusteringcited by 56
  24. 2019Algorithms for Fairness in Sequential Decision Makingcited by 53
  25. 2019Coresets for Clustering with Fairness Constraintscited by 51
  26. 2015On the relation between accuracy and fairness in binary classificationcited by 47
  27. 2018Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Makingcited by 46
  28. 2019Average Individual Fairness: Algorithms, Generalization and Experimentscited by 46
  29. 2019AdaFair: Cumulative Fairness Adaptive Boostingcited by 45
  30. 2020Fair clustering via equitable group representationscited by 43
  31. 2018Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Datacited by 39
  32. 2021FARF: A Fair and Adaptive Random Forests Classifiercited by 37
  33. 2020Probabilistic Fair Clusteringcited by 28
  34. 2018Taking Advantage of Multitask Learning for Fair Classificationcited by 24
  35. 2020Making Fair ML Software using Trustworthy Explanationcited by 24
  36. 2019Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Datacited by 23
  37. 2020How fair can we go in machine learning? Assessing the boundaries of fairness in decision treescited by 22
  38. 2019Fairness in Clustering with Multiple Sensitive Attributescited by 20

50 most cited of 63 All 63 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 32; 2023: 34; 2024: 36; 2025: 38; 2026: 13.

157 papers.

Sort:

By area:

  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2022Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization?cited by 89
  3. 2024Fairness in Large Language Models: A Taxonomic Surveycited by 57
  4. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  5. 2024Advancing Video Anomaly Detection: A Concise Review and a New Datasetcited by 48
  6. 2022Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluationcited by 39
  7. 2023FFB: A Fair Fairness Benchmark for In-Processing Group Fairness Methodscited by 33
  8. 2022Disparate Impact in Differential Privacy from Gradient Misalignmentcited by 31
  9. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  10. 2022Fair Generative Models via Transfer Learningcited by 27
  11. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  12. 2022Fairness and Explainability: Bridging the Gap Towards Fair Model Explanationscited by 22
38 more
  1. 2023FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicitycited by 22
  2. 2022Longitudinal Fairness with Censorshipcited by 21
  3. 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
  4. 2022A Survey on Fairness for Machine Learning on Graphscited by 20
  5. 2023Fairness of ChatGPTcited by 19
  6. 2024Fairness and Bias in Multimodal AI: A Surveycited by 19
  7. 2022How unfair is private learning ?cited by 18
  8. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  9. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  10. 2022Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methodscited by 15
  11. 2024FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applicationscited by 15
  12. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  13. 2023Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinkingcited by 14
  14. 2022Fair and Green Hyperparameter Optimization via Multi-objective and Multiple Information Source Bayesian Optimizationcited by 13
  15. 2023Fairness of ChatGPT and the Role Of Explainable-Guided Promptscited by 13
  16. 2022Optimal Transport of Classifiers to Fairnesscited by 12
  17. 2023Preventing Discriminatory Decision-making in Evolving Data Streamscited by 12
  18. 2023One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisionscited by 12
  19. 2024Balanced Mixed-Type Tabular Data Synthesis with Diffusion Modelscited by 12
  20. 2024Fairness and Bias Mitigation in Computer Vision: A Surveycited by 12
  21. 2021Quasi-Newton Methods for Saddle Point Problems and Beyondcited by 10
  22. 2022Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learningcited by 10
  23. 2024Fairness Definitions in Language Models Explainedcited by 10
  24. 2021Fair Tree Classifier using Strong Demographic Paritycited by 9
  25. 2021Fairness for AUC via Feature Augmentationcited by 9
  26. 2022"Explain it in the Same Way!" -- Model-Agnostic Group Fairness of Counterfactual Explanationscited by 9
  27. 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learningcited by 8
  28. 2025Towards Fair In-Context Learning with Tabular Foundation Modelscited by 8
  29. 2022Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrencescited by 7
  30. 2022Bias and unfairness in machine learning models: a systematic literature reviewcited by 7
  31. 2023Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Modelscited by 7
  32. 2024Classification Under Strategic Self-Selectioncited by 7
  33. 2022Fair Classification via Domain Adaptation: A Dual Adversarial Learning Approachcited by 6
  34. 2022ABCinML: Anticipatory Bias Correction in Machine Learning Applicationscited by 6
  35. 2023(Un)fair devices: Moving beyond AI accuracy in personal sensingcited by 6
  36. 2023Strong statistical parity through fair synthetic datacited by 6
  37. 2024FairJob: A Real-World Dataset for Fairness in Online Systemscited by 6
  38. 2025PAC Learning with Improvementscited by 6

50 most cited of 157 All 157 (JSON)