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Empirical Risk Minimization under Fairness Constraints

arXiv 1802.08626 ↗First posted 23 Feb 2018stat.ML cs.LGcited by 226

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Abstract (arXiv)

We address the problem of algorithmic fairness: ensuring that sensitive variables do not unfairly influence the outcome of a classifier. We present an approach based on empirical risk minimization, which incorporates a fairness constraint into the learning problem. It encourages the conditional risk of the learned classifier to be approximately constant with respect to the sensitive variable. We derive both risk and fairness bounds that support the statistical consistency of our approach. We specify our approach to kernel methods and observe that the fairness requirement implies an orthogonality constraint which can be easily added to these methods. We further observe that for linear models the constraint translates into a simple data preprocessing step. Experiments indicate that the method is empirically effective and performs favorably against state-of-the-art approaches.

Key facts. Cited by 226 papers in the map (data through 14 Oct 2026). First posted on arXiv 23 Feb 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 54; 2022: 58; 2023: 41; 2024: 24; 2025: 32; 2026: 17.

226 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2021Label-Imbalanced and Group-Sensitive Classification under Overparameterizationcited by 74
  3. 2021Mitigating Political Bias in Language Models Through Reinforced Calibrationcited by 73
  4. 2021Characterizing Fairness Over the Set of Good Models Under Selective Labelscited by 65
  5. 2021Causally motivated Shortcut Removal Using Auxiliary Labelscited by 55
  6. 2022Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifierscited by 53
  7. 2022AutoBalance: Optimized Loss Functions for Imbalanced Datacited by 49
  8. 2022FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processingcited by 49
  9. 2021Improving Conditional Coverage via Orthogonal Quantile Regressioncited by 48
  10. 2021Unsupervised Learning of Debiased Representations with Pseudo-Attributescited by 44
  11. 2021On Tilted Losses in Machine Learning: Theory and Applicationscited by 43
  12. 2021Constrained Learning with Non-Convex Lossescited by 42
38 more
  1. 2022Achieving Fairness at No Utility Cost via Data Reweighing with Influencecited by 38
  2. 2021Stateful Strategic Regressioncited by 32
  3. 2022On Learning Fairness and Accuracy on Multiple Subgroupscited by 32
  4. 2021Simple and near-optimal algorithms for hidden stratification and multi-group learningcited by 28
  5. 2021A Stochastic Optimization Framework for Fair Risk Minimizationcited by 26
  6. 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
  7. 2022Rethinking Bias Mitigation: Fairer Architectures Make for Fairer Face Recognitioncited by 26
  8. 2021Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Valuescited by 24
  9. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  10. 2021Assessing Fairness in the Presence of Missing Datacited by 24
  11. 2021Testing Group Fairness via Optimal Transport Projectionscited by 23
  12. 2021Multi-objective Asynchronous Successive Halvingcited by 23
  13. 2022Marrying Fairness and Explainability in Supervised Learningcited by 23
  14. 2023FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysiscited by 23
  15. 2021Improving Fairness for Data Valuation in Horizontal Federated Learningcited by 22
  16. 2022Optimising Equal Opportunity Fairness in Model Trainingcited by 22
  17. 2022Transferring Fairness under Distribution Shifts via Fair Consistency Regularizationcited by 21
  18. 2022FARE: Provably Fair Representation Learning with Practical Certificatescited by 21
  19. 2021Technical Challenges for Training Fair Neural Networkscited by 20
  20. 2022Learning fair representation with a parametric integral probability metriccited by 20
  21. 2021Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloudcited by 19
  22. 2021Poisoning Attacks on Fair Machine Learningcited by 19
  23. 2022What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspectivecited by 18
  24. 2023Unprocessing Seven Years of Algorithmic Fairnesscited by 18
  25. 2021Removing biased data to improve fairness and accuracycited by 17
  26. 2022Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metriccited by 17
  27. 2023Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labelscited by 17
  28. 2021Robust Semantic Interpretability: Revisiting Concept Activation Vectorscited by 16
  29. 2022Beyond Adult and COMPAS: Fairness in Multi-Class Predictioncited by 16
  30. 2022Minimax AUC Fairness: Efficient Algorithm with Provable Convergencecited by 16
  31. 2022Stochastic Differentially Private and Fair Learningcited by 16
  32. 2023Can Fairness be Automated? Guidelines and Opportunities for Fairness-aware AutoMLcited by 16
  33. 2023Fairness through Aleatoric Uncertaintycited by 16
  34. 2023FRAPPE: A Group Fairness Framework for Post-Processing Everythingcited by 16
  35. 2023Resilient Constrained Learningcited by 15
  36. 2024FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applicationscited by 15
  37. 2022Omnipredictors for Constrained Optimizationcited by 14
  38. 2023Fairness in Multi-Task Learning via Wasserstein Barycenterscited by 14

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