mlsanityData to 7 Oct 2026

Equalized odds postprocessing under imperfect group information

arXiv 1906.03284 ↗First posted 7 Jun 2019stat.ML cs.LGcited by 48

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Abstract (arXiv)

Most approaches aiming to ensure a model's fairness with respect to a protected attribute (such as gender or race) assume to know the true value of the attribute for every data point. In this paper, we ask to what extent fairness interventions can be effective even when only imperfect information about the protected attribute is available. In particular, we study the prominent equalized odds postprocessing method of Hardt et al. (2016) under a perturbation of the attribute. We identify conditions on the perturbation that guarantee that the bias of a classifier is reduced even by running equalized odds with the perturbed attribute. We also study the error of the resulting classifier. We empirically observe that under our identified conditions most often the error does not suffer from a perturbation of the protected attribute. For a special case, we formally prove this observation to be true.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2019.

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Citing papers per year: 2021: 12; 2022: 9; 2023: 9; 2024: 10; 2025: 5; 2026: 3.

48 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021Sample Selection for Fair and Robust Trainingcited by 48
  3. 2021Your fairness may vary: Pretrained language model fairness in toxic text classificationcited by 38
  4. 2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33
  5. 2022Fairness Reprogrammingcited by 33
  6. 2021Fairness for Image Generation with Uncertain Sensitive Attributescited by 31
  7. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  8. 2021Fair Classification with Adversarial Perturbationscited by 23
  9. 2021Fairness-Aware PAC Learning from Corrupted Datacited by 18
  10. 2021Multiaccurate Proxies for Downstream Fairnesscited by 17
  11. 2022Fair Ranking with Noisy Protected Attributescited by 17
  12. 2021Fair Sequential Selection Using Supervised Learning Modelscited by 16
36 more
  1. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  2. 2022FAIR-FATE: Fair Federated Learning with Momentumcited by 10
  3. 2021Measuring Fairness Under Unawareness of Sensitive Attributes: A Quantification-Based Approachcited by 9
  4. 2024FairJob: A Real-World Dataset for Fairness in Online Systemscited by 6
  5. 2024Exploring Bias and Prediction Metrics to Characterise the Fairness of Machine Learning for Equity-Centered Public Health Decision-Making: A Narrative Reviewcited by 6
  6. 2021FairBalance: How to Achieve Equalized Odds With Data Pre-processingcited by 5
  7. 2022Estimating and Controlling for Equalized Odds via Sensitive Attribute Predictorscited by 5
  8. 2022Breaking Fair Binary Classification with Optimal Flipping Attackscited by 4
  9. 2023Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairnesscited by 4
  10. 2023Fairness in Ranking under Disparate Uncertaintycited by 4
  11. 2023Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learningcited by 4
  12. 2024Stability and Multigroup Fairness in Ranking with Uncertain Predictionscited by 4
  13. 2024Learning With Multi-Group Guarantees For Clusterable Subpopulationscited by 3
  14. 2025Fair Text Classification via Transferable Representationscited by 3
  15. 2021Hard Choices and Hard Limits for Artificial Intelligencecited by 2
  16. 2021Dataset Bias Mitigation Through Analysis of CNN Training Scorescited by 2
  17. 2022How Robust is your Fair Model? Exploring the Robustness of Diverse Fairness Strategiescited by 2
  18. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
  19. 2023Group Fairness with Uncertainty in Sensitive Attributescited by 2
  20. 2023Gender Bias in Transformer Models: A comprehensive surveycited by 2
  21. 2023Sampling Individually-Fair Rankings that are Always Group Faircited by 2
  22. 2023Fair Active Learning in Low-Data Regimescited by 2
  23. 2024Multi-Output Distributional Fairness via Post-Processingcited by 2
  24. 2024Fairness in Ranking: Robustness through Randomization without the Protected Attributecited by 1
  25. 2024Robust Fair Clustering with Group Membership Uncertainty Setscited by 1
  26. 2024Fair Clustering: Critique, Caveats, and Future Directionscited by 1
  27. 2025Multiaccuracy and Multicalibration via Proxy Groupscited by 1
  28. 2022Are Your Reviewers Being Treated Equally? Discovering Subgroup Structures to Improve Fairness in Spam Detectioncited by 0
  29. 2023Integrating Fairness and Model Pruning Through Bi-level Optimizationcited by 0
  30. 2024Text-to-Image Representativity Fairness Evaluation Frameworkcited by 0
  31. 2025FairUDT: Fairness-aware Uplift Decision Treescited by 0
  32. 2025FairSAM: Fair Classification on Corrupted Image Data Through Sharpness-Aware Minimizationcited by 0
  33. 2025FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utilitycited by 0
  34. 2026Rank-Constrained Adaptation for Reliable Real-World Performancecited by 0
  35. 2026Geometry of Relaxed Fair Regression: A Unified Framework for Aware and Unaware Settingscited by 0
  36. 2026Beyond Aggregate Calibration: Decomposing Income-Conditional Recall Disparities in Automated Credit Default Predictioncited by 0