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Pairwise Fairness for Ranking and Regression

arXiv 1906.05330 ↗First posted 12 Jun 2019cs.LG stat.MLcited by 58

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Abstract (arXiv)

We present pairwise fairness metrics for ranking models and regression models that form analogues of statistical fairness notions such as equal opportunity, equal accuracy, and statistical parity. Our pairwise formulation supports both discrete protected groups, and continuous protected attributes. We show that the resulting training problems can be efficiently and effectively solved using existing constrained optimization and robust optimization techniques developed for fair classification. Experiments illustrate the broad applicability and trade-offs of these methods.

Key facts. Cited by 58 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jun 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 15; 2022: 20; 2023: 6; 2024: 6; 2025: 7; 2026: 4.

58 papers.

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  1. 2021A survey on datasets for fairness-aware machine learningcited by 157
  2. 2021User-oriented Fairness in Recommendationcited by 75
  3. 2021Personalized Counterfactual Fairness in Recommendationcited by 38
  4. 2021Fairness in Ranking under Uncertaintycited by 27
  5. 2022Fairness in Recommendation: Foundations, Methods and Applicationscited by 27
  6. 2021Maxmin-Fair Ranking: Individual Fairness under Group-Fairness Constraintscited by 18
  7. 2021Understanding and Mitigating Accuracy Disparity in Regressioncited by 17
  8. 2021Deep ROC Analysis and AUC as Balanced Average Accuracy to Improve Model Selection, Understanding and Interpretationcited by 17
  9. 2022Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcarecited by 16
  10. 2022Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecited by 16
  11. 2022Experiments on Generalizability of User-Oriented Fairness in Recommender Systemscited by 13
  12. 2022Reinforced Path Reasoning for Counterfactual Explainable Recommendationcited by 13
38 more
  1. 2021A comparison of approaches to improve worst-case predictive model performance over patient subpopulationscited by 12
  2. 2022Introducing the Expohedron for Efficient Pareto-optimal Fairness-Utility Amortizations in Repeated Rankingscited by 11
  3. 2022Stochastic Methods for AUC Optimization subject to AUC-based Fairness Constraintscited by 9
  4. 2021Fairness Through Regularization for Learning to Rankcited by 8
  5. 2021Costs and Benefits of Fair Regressioncited by 8
  6. 2021Pairwise Fairness for Ordinal Regressioncited by 6
  7. 2021Fairness for Robust Learning to Rankcited by 6
  8. 2022Demographic Parity Constrained Minimax Optimal Regression under Linear Modelcited by 6
  9. 202410 Years of Fair Representations: Challenges and Opportunitiescited by 6
  10. 2025Preserving AUC Fairness in Learning with Noisy Protected Groupscited by 6
  11. 2022Fair Group-Shared Representations with Normalizing Flowscited by 5
  12. 2022Learning Distributionally Robust Models at Scale via Composite Optimizationcited by 5
  13. 2022Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluationscited by 5
  14. 2023Fairness and Sequential Decision Making: Limits, Lessons, and Opportunitiescited by 5
  15. 2023Bipartite Ranking Fairness through a Model Agnostic Ordering Adjustmentcited by 5
  16. 2024Regression under demographic parity constraints via unlabeled post-processingcited by 5
  17. 2022Normalise for Fairness: A Simple Normalisation Technique for Fairness in Regression Machine Learning Problemscited by 4
  18. 2022Invariant Representations with Stochastically Quantized Neural Networkscited by 4
  19. 2023Fairness in Ranking under Disparate Uncertaintycited by 4
  20. 2023Group-blind optimal transport to group parity and its constrained variantscited by 4
  21. 2024Stability and Multigroup Fairness in Ranking with Uncertain Predictionscited by 4
  22. 2025Evaluating and Mitigating Bias in AI-Based Medical Text Generationcited by 4
  23. 2021Fair Regression under Sample Selection Biascited by 3
  24. 2021A Pre-processing Method for Fairness in Rankingcited by 3
  25. 2022Fair Interpretable Representation Learning with Correction Vectorscited by 3
  26. 2022MANI-Rank: Multiple Attribute and Intersectional Group Fairness for Consensus Rankingcited by 2
  27. 2022Equal Experience in Recommender Systemscited by 2
  28. 2022Properties of Group Fairness Metrics for Rankingscited by 2
  29. 2023Max-Min Diversification with Fairness Constraints: Exact and Approximation Algorithmscited by 2
  30. 2023Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraintscited by 2
  31. 2024Demographic parity in regression and classification within the unawareness frameworkcited by 2
  32. 2024From Efficiency to Equity: Measuring Fairness in Preference Learningcited by 2
  33. 2025Enforcing Fair Predicted Scores on Intervals of Percentiles by Difference-of-Convex Constraintscited by 2
  34. 2021Regularization Strategies for Quantile Regressioncited by 1
  35. 2022Fair Interpretable Learning via Correction Vectorscited by 1
  36. 2022Cumulative Stay-time Representation for Electronic Health Records in Medical Event Time Predictioncited by 1
  37. 2024Improving Fairness of Automated Chest X-ray Diagnosis by Contrastive Learningcited by 1
  38. 2025Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneitycited by 1

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