mlsanityData to 14 Oct 2026

Sample Selection for Fair and Robust Training

arXiv 2110.14222 ↗First posted 27 Oct 2021cs.LG cs.AI stat.MLcited by 48

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Abstract (arXiv)

Fairness and robustness are critical elements of Trustworthy AI that need to be addressed together. Fairness is about learning an unbiased model while robustness is about learning from corrupted data, and it is known that addressing only one of them may have an adverse affect on the other. In this work, we propose a sample selection-based algorithm for fair and robust training. To this end, we formulate a combinatorial optimization problem for the unbiased selection of samples in the presence of data corruption. Observing that solving this optimization problem is strongly NP-hard, we propose a greedy algorithm that is efficient and effective in practice. Experiments show that our algorithm obtains fairness and robustness that are better than or comparable to the state-of-the-art technique, both on synthetic and benchmark real datasets. Moreover, unlike other fair and robust training baselines, our algorithm can be used by only modifying the sampling step in batch selection without changing the training algorithm or leveraging additional clean data.

Key facts. Cited by 48 papers in the map (data through 14 Oct 2026). Builds on 44 of its 73 references that are in the map. First posted on arXiv 27 Oct 2021.

Builds on

44 of 73 references.

References · 44

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2016Equality of Opportunity in Supervised Learningcited by 2,007
  3. 2018Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labelscited by 1,059
  4. 2017Counterfactual Fairnesscited by 804
  5. 2014Certifying and removing disparate impactcited by 715
  6. 2018Learning to Reweight Examples for Robust Deep Learningcited by 675
  7. 2016Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approachcited by 665
  8. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  9. 2017MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labelscited by 653
  10. 2018A Reductions Approach to Fair Classificationcited by 587
  11. 2020Learning from Noisy Labels with Deep Neural Networks: A Surveycited by 582
  12. 2017Regularizing Neural Networks by Penalizing Confident Output Distributionscited by 464
32 more
  1. 2017On Fairness and Calibrationcited by 406
  2. 2019Using Pre-Training Can Improve Model Robustness and Uncertaintycited by 373
  3. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 369
  4. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 364
  5. 2019Unsupervised Label Noise Modeling and Loss Correctioncited by 328
  6. 2020Fairness without Demographics through Adversarially Reweighted Learningcited by 251
  7. 2019Understanding and Utilizing Deep Neural Networks Trained with Noisy Labelscited by 209
  8. 2017Avoiding Discrimination through Causal Reasoningcited by 194
  9. 2018Dimensionality-Driven Learning with Noisy Labelscited by 173
  10. 2017Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samplescited by 147
  11. 2019Identifying and Correcting Label Bias in Machine Learningcited by 146
  12. 2017Fair Inference On Outcomescited by 144
  13. 2019Learning From Noisy Labels By Regularized Estimation Of Annotator Confusioncited by 127
  14. 2019Fair Generative Modeling via Weak Supervisioncited by 114
  15. 2020FairBatch: Batch Selection for Model Fairnesscited by 99
  16. 2015Webly Supervised Learning of Convolutional Networkscited by 95
  17. 2018Masking: A New Perspective of Noisy Supervisioncited by 93
  18. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
  19. 2018Label Sanitization against Label Flipping Poisoning Attackscited by 73
  20. 2019Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classificationcited by 60
  21. 2020Poisoning Attacks on Algorithmic Fairnesscited by 59
  22. 2020Robust Fairness under Covariate Shiftcited by 58
  23. 2018Two-Player Games for Efficient Non-Convex Constrained Optimizationcited by 55
  24. 2020FR-Train: A Mutual Information-Based Approach to Fair and Robust Trainingcited by 52
  25. 2020Removing Spurious Features can Hurt Accuracy and Affect Groups Disproportionatelycited by 52
  26. 2019Equalized odds postprocessing under imperfect group informationcited by 48
  27. 2019Noise-tolerant fair classificationcited by 40
  28. 2020On Adversarial Bias and the Robustness of Fair Machine Learningcited by 36
  29. 2020Ensuring Fairness Beyond the Training Datacited by 35
  30. 2019Fairness in Algorithmic Decision Making: An Excursion Through the Lens of Causalitycited by 33
  31. 2017Learning Deep Networks from Noisy Labels with Dropout Regularizationcited by 32
  32. 2021Robust Fairness-aware Learning Under Sample Selection Biascited by 9

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 13; 2023: 17; 2024: 10; 2025: 5; 2026: 2.

48 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  3. 2022FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clientscited by 46
  4. 2022The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networkscited by 41
  5. 2024ScaleBiO: Scalable Bilevel Optimization for LLM Data Reweightingcited by 37
  6. 2023A Trip Towards Fairness: Bias and De-Biasing in Large Language Modelscited by 32
  7. 2022Fairness Transferability Subject to Bounded Distribution Shiftcited by 28
  8. 2022Rank-based Decomposable Losses in Machine Learning: A Surveycited by 26
  9. 2024Can LLMs Recognize Toxicity? A Structured Investigation Framework and Toxicity Metriccited by 23
  10. 2022DC-Check: A Data-Centric AI checklist to guide the development of reliable machine learning systemscited by 14
  11. 2022FORML: Learning to Reweight Data for Fairnesscited by 12
  12. 2022Certifying Some Distributional Fairness with Subpopulation Decompositioncited by 11
36 more
  1. 2022Input-agnostic Certified Group Fairness via Gaussian Parameter Smoothingcited by 11
  2. 2023Racial Bias within Face Recognition: A Surveycited by 9
  3. 2023Understanding Fairness Surrogate Functions in Algorithmic Fairnesscited by 8
  4. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  5. 2022Segmenting across places: The need for fair transfer learning with satellite imagerycited by 6
  6. 2023Data Optimization in Deep Learning: A Surveycited by 6
  7. 2023Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Setscited by 6
  8. 2024Falcon: Fair Active Learning using Multi-armed Banditscited by 6
  9. 2024RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Taskscited by 6
  10. 2025Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyondcited by 6
  11. 2023RetouchingFFHQ: A Large-scale Dataset for Fine-grained Face Retouching Detectioncited by 5
  12. 2023FairWASP: Fast and Optimal Fair Wasserstein Pre-processingcited by 5
  13. 2023When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attackscited by 5
  14. 2022Breaking Fair Binary Classification with Optimal Flipping Attackscited by 4
  15. 2022Fairness-aware Regression Robust to Adversarial Attackscited by 4
  16. 2023An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applicationscited by 4
  17. 2024LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Viewscited by 4
  18. 2023On the Fairness ROAD: Robust Optimization for Adversarial Debiasingcited by 3
  19. 2023To be Robust and to be Fair: Aligning Fairness with Robustnesscited by 2
  20. 2023Saturn: An Optimized Data System for Large Model Deep Learning Workloadscited by 2
  21. 2024Specification Overfitting in Artificial Intelligencecited by 2
  22. 2024Multigroup Robustnesscited by 2
  23. 2022I Prefer not to Say: Protecting User Consent in Models with Optional Personal Datacited by 1
  24. 2023Boosting Fair Classifier Generalization through Adaptive Priority Reweighingcited by 1
  25. 2023Causal Fairness-Guided Dataset Reweighting using Neural Networkscited by 1
  26. 2025Stress-Testing ML Pipelines with Adversarial Data Corruptioncited by 1
  27. 2023Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognitioncited by 0
  28. 2023Fairguard: Harness Logic-based Fairness Rules in Smart Citiescited by 0
  29. 2024VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AIcited by 0
  30. 2024The Benefits and Risks of Transductive Approaches for AI Fairnesscited by 0
  31. 2024Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimagingcited by 0
  32. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  33. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  34. 2025From Fragile to Certified: Wasserstein Audits of Group Fairness Under Distribution Shiftcited by 0
  35. 2026A Prototypical Signature Approach for Writer-Independent Offline Signature Verificationcited by 0
  36. 2026A Geometric Theory of Robust Fairness Auditscited by 0