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A Convex Framework for Fair Regression

arXiv 1706.02409 ↗First posted 7 Jun 2017cs.LG stat.MLcited by 147

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Abstract (arXiv)

We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the rang from notions of group fairness to strong individual fairness. By varying the weight on the fairness regularizer, we can compute the efficient frontier of the accuracy-fairness trade-off on any given dataset, and we measure the severity of this trade-off via a numerical quantity we call the Price of Fairness (PoF). The centerpiece of our results is an extensive comparative study of the PoF across six different datasets in which fairness is a primary consideration.

Key facts. Cited by 147 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Jun 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 34; 2022: 28; 2023: 36; 2024: 25; 2025: 16; 2026: 8.

147 papers.

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  1. 2021A survey on datasets for fairness-aware machine learningcited by 157
  2. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  3. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  4. 2021Trustworthy AI: A Computational Perspectivecited by 93
  5. 2021Socially Responsible AI Algorithms: Issues, Purposes, and Challengescited by 56
  6. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  7. 2022Perturbation Augmentation for Fairer NLPcited by 51
  8. 2021Survey: Leakage and Privacy at Inference Timecited by 44
  9. 2021Constrained Learning with Non-Convex Lossescited by 42
  10. 2021Selective Regression Under Fairness Criteriacited by 34
  11. 2023Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectivescited by 30
  12. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
38 more
  1. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  2. 2023Fair Machine Unlearning: Data Removal while Mitigating Disparitiescited by 25
  3. 2022A Lagrangian Duality Approach to Active Learningcited by 24
  4. 2024Deconstructing The Ethics of Large Language Models from Long-standing Issues to New-emerging Dilemmas: A Surveycited by 23
  5. 2021When Fair Ranking Meets Uncertain Inferencecited by 22
  6. 2022Conformalized Fairness via Quantile Regressioncited by 19
  7. 2021The Authors Matter: Understanding and Mitigating Implicit Bias in Deep Text Classificationcited by 18
  8. 2021Loss-Aversively Fair Classificationcited by 18
  9. 2022Adaptive Sampling Strategies to Construct Equitable Training Datasetscited by 18
  10. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  11. 2023The Possibility of Fairness: Revisiting the Impossibility Theorem in Practicecited by 18
  12. 2021Removing biased data to improve fairness and accuracycited by 17
  13. 2021Probabilistic Verification of Neural Networks Against Group Fairnesscited by 17
  14. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  15. 2022Subgroup Robustness Grows On Trees: An Empirical Baseline Investigationcited by 17
  16. 2023Runtime Monitoring of Dynamic Fairness Propertiescited by 17
  17. 2022Learning Optimal Fair Classification Trees: Trade-offs Between Interpretability, Fairness, and Accuracycited by 15
  18. 2021Achieving Fairness with a Simple Ridge Penaltycited by 13
  19. 2021FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomescited by 13
  20. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  21. 2021Transitioning from Real to Synthetic data: Quantifying the bias in modelcited by 12
  22. 2021FairCanary: Rapid Continuous Explainable Fairnesscited by 12
  23. 2022Fair Data Representation for Machine Learning at the Pareto Frontiercited by 12
  24. 2022FORML: Learning to Reweight Data for Fairnesscited by 12
  25. 2022Fair Generalized Linear Models with a Convex Penaltycited by 11
  26. 2021Interpretable and Fair Boolean Rule Sets via Column Generationcited by 10
  27. 2022Attainability and Optimality: The Equalized Odds Fairness Revisitedcited by 10
  28. 2022To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontiercited by 10
  29. 2023Mitigating Group Bias in Federated Learning: Beyond Local Fairnesscited by 10
  30. 2026Machine Unlearning under Retain-Forget Entanglementcited by 10
  31. 2023Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Modelscited by 9
  32. 2024Fairness in Reinforcement Learning: A Surveycited by 9
  33. 2021Costs and Benefits of Fair Regressioncited by 8
  34. 2022Fair admission risk prediction with proportional multicalibrationcited by 8
  35. 2022Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learningcited by 8
  36. 2023Monitoring Algorithmic Fairnesscited by 8
  37. 2023Understanding Fairness Surrogate Functions in Algorithmic Fairnesscited by 8
  38. 2024The Role of Learning Algorithms in Collective Actioncited by 8

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