mlsanityData to 7 Oct 2026

Minimax Pareto Fairness: A Multi Objective Perspective

arXiv 2011.01821 ↗First posted 3 Nov 2020stat.ML cs.LGcited by 152

Not on the map. Why?

Abstract (arXiv)

In this work we formulate and formally characterize group fairness as a multi-objective optimization problem, where each sensitive group risk is a separate objective. We propose a fairness criterion where a classifier achieves minimax risk and is Pareto-efficient w.r.t. all groups, avoiding unnecessary harm, and can lead to the best zero-gap model if policy dictates so. We provide a simple optimization algorithm compatible with deep neural networks to satisfy these constraints. Since our method does not require test-time access to sensitive attributes, it can be applied to reduce worst-case classification errors between outcomes in unbalanced classification problems. We test the proposed methodology on real case-studies of predicting income, ICU patient mortality, skin lesions classification, and assessing credit risk, demonstrating how our framework compares favorably to other approaches.

Key facts. Cited by 152 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Nov 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 27; 2022: 36; 2023: 30; 2024: 26; 2025: 20; 2026: 13.

152 papers.

Sort:

By area:

  1. 2022Correct-N-Contrast: A Contrastive Approach for Improving Robustness to Spurious Correlationscited by 171
  2. 2021A survey on datasets for fairness-aware machine learningcited by 157
  3. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  4. 2021Towards Unbiased and Accurate Deferral to Multiple Expertscited by 71
  5. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 66
  6. 2021The Spotlight: A General Method for Discovering Systematic Errors in Deep Learning Modelscited by 63
  7. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  8. 2022Improving the Fairness of Chest X-ray Classifierscited by 52
  9. 2021Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learningcited by 50
  10. 2024Smooth Tchebycheff Scalarization for Multi-Objective Optimizationcited by 50
  11. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  12. 2021Fair Normalizing Flowscited by 38
38 more
  1. 2022Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overviewcited by 37
  2. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  3. 2021The Rich Get Richer: Disparate Impact of Semi-Supervised Learningcited by 28
  4. 2022Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognitioncited by 26
  5. 2023Privacy and Fairness in Federated Learning: on the Perspective of Trade-offcited by 26
  6. 2021BARACK: Partially Supervised Group Robustness With Guaranteescited by 25
  7. 2023Fair Machine Unlearning: Data Removal while Mitigating Disparitiescited by 25
  8. 2021Adaptive Sampling for Minimax Fair Classificationcited by 24
  9. 2022Minimax Demographic Group Fairness in Federated Learningcited by 24
  10. 2022Differentially Private SGDA for Minimax Problemscited by 23
  11. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
  12. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  13. 2021Technical Challenges for Training Fair Neural Networkscited by 20
  14. 2022An Algorithmic Framework for Bias Bountiescited by 19
  15. 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
  16. 2021Latent Space Smoothing for Individually Fair Representationscited by 18
  17. 2022Evaluating Proposed Fairness Models for Face Recognition Algorithmscited by 18
  18. 2021Multiaccurate Proxies for Downstream Fairnesscited by 17
  19. 2022Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcarecited by 16
  20. 2022Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecited by 16
  21. 2021MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Datasetcited by 15
  22. 2023Out of Context: Investigating the Bias and Fairness Concerns of "Artificial Intelligence as a Service"cited by 15
  23. 2025Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyondcited by 15
  24. 2022Stability and Generalization for Markov Chain Stochastic Gradient Methodscited by 14
  25. 2022FIFA: Making Fairness More Generalizable in Classifiers Trained on Imbalanced Datacited by 13
  26. 2023Learning Personalized Decision Support Policiescited by 13
  27. 2021A Unified Approach to Fair Online Learning via Blackwell Approachabilitycited by 12
  28. 2021A comparison of approaches to improve worst-case predictive model performance over patient subpopulationscited by 12
  29. 2021On Characterizing the Trade-off in Invariant Representation Learningcited by 12
  30. 2022Fair Federated Learning via Bounded Group Losscited by 12
  31. 2024Fairness Without Harm: An Influence-Guided Active Sampling Approachcited by 12
  32. 2024OxonFair: A Flexible Toolkit for Algorithmic Fairnesscited by 12
  33. 2021Robustness Disparities in Commercial Face Detectioncited by 11
  34. 2022Bounding and Approximating Intersectional Fairness through Marginal Fairnesscited by 11
  35. 2022How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shiftscited by 11
  36. 2022Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descentcited by 11
  37. 2024Addressing Polarization and Unfairness in Performative Predictioncited by 11
  38. 2022To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontiercited by 10

50 most cited of 152 All 152 (JSON)