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Training individually fair ML models with Sensitive Subspace Robustness

arXiv 1907.00020 ↗First posted 28 Jun 2019stat.ML cs.LGcited by 73

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Abstract (arXiv)

We consider training machine learning models that are fair in the sense that their performance is invariant under certain sensitive perturbations to the inputs. For example, the performance of a resume screening system should be invariant under changes to the gender and/or ethnicity of the applicant. We formalize this notion of algorithmic fairness as a variant of individual fairness and develop a distributionally robust optimization approach to enforce it during training. We also demonstrate the effectiveness of the approach on two ML tasks that are susceptible to gender and racial biases.

Key facts. Cited by 73 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Jun 2019.

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Citing papers per year: 2021: 14; 2022: 28; 2023: 15; 2024: 11; 2025: 4; 2026: 1.

73 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021Fair Mixup: Fairness via Interpolationcited by 109
  3. 2023Fast Model Debias with Machine Unlearningcited by 72
  4. 2021Post-processing for Individual Fairnesscited by 61
  5. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  6. 2021Learning Fair Classifiers with Partially Annotated Group Labelscited by 40
  7. 2021Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimizationcited by 32
  8. 2022Individual Fairness Guarantees for Neural Networkscited by 23
  9. 2022Optimising Equal Opportunity Fairness in Model Trainingcited by 22
  10. 2022Fairify: Fairness Verification of Neural Networkscited by 22
  11. 2022De-biasing "bias" measurementcited by 20
  12. 2023Re-weighting Based Group Fairness Regularization via Classwise Robust Optimizationcited by 20
38 more
  1. 2021Latent Space Smoothing for Individually Fair Representationscited by 18
  2. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  3. 2024CEB: Compositional Evaluation Benchmark for Fairness in Large Language Modelscited by 18
  4. 2022CertiFair: A Framework for Certified Global Fairness of Neural Networkscited by 17
  5. 2022Accurate Fairness: Improving Individual Fairness without Trading Accuracycited by 16
  6. 2021Statistical inference for individual fairnesscited by 13
  7. 2022Domain Adaptation meets Individual Fairness. And they get alongcited by 13
  8. 2024FairProof : Confidential and Certifiable Fairness for Neural Networkscited by 13
  9. 2024Fairness and Bias Mitigation in Computer Vision: A Surveycited by 13
  10. 2021Individually Fair Gradient Boostingcited by 12
  11. 2023Chasing Fairness Under Distribution Shift: A Model Weight Perturbation Approachcited by 12
  12. 2021A Fairness Analysis on Private Aggregation of Teacher Ensemblescited by 11
  13. 2021Fairness with Continuous Optimal Transportcited by 10
  14. 2021Wasserstein Robust Classification with Fairness Constraintscited by 10
  15. 2022Towards A Holistic View of Bias in Machine Learning: Bridging Algorithmic Fairness and Imbalanced Learningcited by 10
  16. 2021Robust Fairness-aware Learning Under Sample Selection Biascited by 9
  17. 2022InfoOT: Information Maximizing Optimal Transportcited by 9
  18. 2022A.I. Robustness: a Human-Centered Perspective on Technological Challenges and Opportunitiescited by 9
  19. 2024AIM: Attributing, Interpreting, Mitigating Data Unfairnesscited by 9
  20. 2024Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibriumcited by 9
  21. 2021Task-Adaptive Neural Network Search with Meta-Contrastive Learningcited by 8
  22. 2022Learning Antidote Data to Individual Unfairnesscited by 8
  23. 2023Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based Perspectivecited by 8
  24. 2023Learning Fair Classifiers via Min-Max F-divergence Regularizationcited by 8
  25. 2021Individually Fair Rankingcited by 7
  26. 2022Private and Reliable Neural Network Inferencecited by 7
  27. 2023Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairnesscited by 7
  28. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  29. 2024Towards Explainability and Fairness in Swiss Judgement Prediction: Benchmarking on a Multilingual Datasetcited by 7
  30. 2024Intrinsic Fairness-Accuracy Tradeoffs under Equalized Oddscited by 7
  31. 2022Metrizing Fairnesscited by 6
  32. 2023Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspectscited by 6
  33. 2022Individually Fair Learning with One-Sided Feedbackcited by 5
  34. 2022iFlipper: Label Flipping for Individual Fairnesscited by 5
  35. 2022Human-Guided Fair Classification for Natural Language Processingcited by 5
  36. 2021Fair Training of Decision Tree Classifierscited by 4
  37. 2022FETA: Fairness Enforced Verifying, Training, and Predicting Algorithms for Neural Networkscited by 4
  38. 2023Retiring DeltaDP: New Distribution-Level Metrics for Demographic Paritycited by 4

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