mlsanityData to 7 Oct 2026

Removing biased data to improve fairness and accuracy

arXiv 2102.03054 ↗First posted 5 Feb 2021cs.LG cs.AI cs.CYcited by 17

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Abstract (arXiv)

Machine learning systems are often trained using data collected from historical decisions. If past decisions were biased, then automated systems that learn from historical data will also be biased. We propose a black-box approach to identify and remove biased training data. Machine learning models trained on such debiased data (a subset of the original training data) have low individual discrimination, often 0%. These models also have greater accuracy and lower statistical disparity than models trained on the full historical data. We evaluated our methodology in experiments using 6 real-world datasets. Our approach outperformed seven previous approaches in terms of individual discrimination and accuracy.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 49 of its 128 references that are in the map. First posted on arXiv 5 Feb 2021.

Builds on

49 of 128 references.

References · 49

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2017Understanding Black-box Predictions via Influence Functionscited by 1,714
  4. 2017Counterfactual Fairnesscited by 802
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  7. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  8. 2018A Reductions Approach to Fair Classificationcited by 585
  9. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  10. 2017On Fairness and Calibrationcited by 406
  11. 2017Algorithmic decision making and the cost of fairnesscited by 402
  12. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
37 more
  1. 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
  2. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  3. 2015The Variational Fair Autoencodercited by 223
  4. 2018A comparative study of fairness-enhancing interventions in machine learningcited by 208
  5. 2015Censoring Representations with an Adversarycited by 191
  6. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  7. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  8. 2019Towards Fairer Datasets: Filtering and Balancing the Distribution of the People Subtree in the ImageNet Hierarchycited by 156
  9. 2017A Convex Framework for Fair Regressioncited by 147
  10. 2018Classification with Fairness Constraints: A Meta-Algorithm with Provable Guaranteescited by 146
  11. 2017Fair Inference On Outcomescited by 143
  12. 2017Fairness Testing: Testing Software for Discriminationcited by 123
  13. 2019Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combinationcited by 79
  14. 2018Achieving Fairness through Adversarial Learning: an Application to Recidivism Predictioncited by 77
  15. 2018Automated Directed Fairness Testingcited by 77
  16. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  17. 2020Learning Certified Individually Fair Representationscited by 69
  18. 2019Operationalizing Individual Fairness with Pairwise Fair Representationscited by 64
  19. 2019Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?cited by 64
  20. 2019Counterfactual Risk Assessments, Evaluation, and Fairnesscited by 59
  21. 2019FlipTest: Fairness Testing via Optimal Transportcited by 51
  22. 2019Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?cited by 50
  23. 2019Average Individual Fairness: Algorithms, Generalization and Experimentscited by 46
  24. 2016Auditing Black-box Models for Indirect Influencecited by 41
  25. 2018Blind Justice: Fairness with Encrypted Sensitive Attributescited by 41
  26. 2019Fair Classification and Social Welfarecited by 40
  27. 2018Fairness Through Causal Awareness: Learning Latent-Variable Models for Biased Datacited by 39
  28. 2016A statistical framework for fair predictive algorithmscited by 37
  29. 2020What's Sex Got To Do With Fair Machine Learning?cited by 37
  30. 2017Penalizing Unfairness in Binary Classificationcited by 25
  31. 2019Awareness in Practice: Tensions in Access to Sensitive Attribute Data for Antidiscriminationcited by 23
  32. 2017Provably Fair Representationscited by 22
  33. 2019An Algorithmic Framework for Fairness Elicitationcited by 15
  34. 2014Learning unbiased featurescited by 10
  35. 2018Deep Weighted Averaging Classifierscited by 9
  36. 2020Fairness-Aware Learning with Prejudice Free Representationscited by 4
  37. 2017Matching Code and Law: Achieving Algorithmic Fairness with Optimal Transportcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 9; 2023: 0; 2024: 4; 2025: 1; 2026: 1.

17 papers.

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  1. 2021ZeroCap: Zero-Shot Image-to-Text Generation for Visual-Semantic Arithmeticcited by 150
  2. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  3. 2022Detecting Shortcut Learning for Fair Medical AI using Shortcut Testingcited by 52
  4. 2022Accurate Fairness: Improving Individual Fairness without Trading Accuracycited by 16
  5. 2022FAIR-FATE: Fair Federated Learning with Momentumcited by 10
  6. 2022Understanding Unfairness in Fraud Detection through Model and Data Bias Interactionscited by 7
  7. 2022Enforcing Delayed-Impact Fairness Guaranteescited by 7
  8. 2024A call for embodied AIcited by 7
  9. 2022Provable Fairness for Neural Network Models using Formal Verificationcited by 6
  10. 2024Towards Fairness and Privacy: A Novel Data Pre-processing Optimization Framework for Non-binary Protected Attributescited by 2
  11. 2021Outlier Detection using AI: A Surveycited by 1
  12. 2022Repairing Brain-Computer Interfaces with Fault-Based Data Acquisitioncited by 1
5 more
  1. 2024Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal Techniquescited by 1
  2. 2022Can Ensembling Pre-processing Algorithms Lead to Better Machine Learning Fairness?cited by 0
  3. 2024De-Biasing Models of Biased Decisions: A Comparison of Methods Using Mortgage Application Datacited by 0
  4. 2025The Illusion of Fairness: Auditing Fairness Interventions with Audit Studiescited by 0
  5. 2026A Blended Likelihood Approach for Achieving Fairness Using Naive Bayescited by 0