mlsanityData to 7 Oct 2026

Accuracy and Fairness Trade-offs in Machine Learning: A Stochastic Multi-Objective Approach

arXiv 2008.01132 ↗First posted 3 Aug 2020cs.LG stat.MLcited by 39

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Abstract (arXiv)

In the application of machine learning to real-life decision-making systems, e.g., credit scoring and criminal justice, the prediction outcomes might discriminate against people with sensitive attributes, leading to unfairness. The commonly used strategy in fair machine learning is to include fairness as a constraint or a penalization term in the minimization of the prediction loss, which ultimately limits the information given to decision-makers. In this paper, we introduce a new approach to handle fairness by formulating a stochastic multi-objective optimization problem for which the corresponding Pareto fronts uniquely and comprehensively define the accuracy-fairness trade-offs. We have then applied a stochastic approximation-type method to efficiently obtain well-spread and accurate Pareto fronts, and by doing so we can handle training data arriving in a streaming way.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 10; 2023: 9; 2024: 7; 2025: 6; 2026: 3.

39 papers.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2023Bias Testing and Mitigation in LLM-based Code Generationcited by 53
  3. 2021Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimizationcited by 32
  4. 2021FADE: FAir Double Ensemble Learning for Observable and Counterfactual Outcomescited by 13
  5. 2022Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descentcited by 11
  6. 2022FAIR-FATE: Fair Federated Learning with Momentumcited by 10
  7. 2023Demystifying Local and Global Fairness Trade-offs in Federated Learning Using Partial Information Decompositioncited by 10
  8. 2024FERERO: A Flexible Framework for Preference-Guided Multi-Objective Learningcited by 9
  9. 2022Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflowscited by 8
  10. 2022Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learningcited by 8
  11. 2021Rule Generation for Classification: Scalability, Interpretability, and Fairnesscited by 4
  12. 2022Towards Fairness-Aware Multi-Objective Optimizationcited by 4
27 more
  1. 2022Generalization In Multi-Objective Machine Learningcited by 4
  2. 2022Quantitative AI Risk Assessments: Opportunities and Challengescited by 4
  3. 2023Group-blind optimal transport to group parity and its constrained variantscited by 4
  4. 2023Earning Extra Performance from Restrictive Feedbackscited by 3
  5. 2023Learning Rate Free Sampling in Constrained Domainscited by 3
  6. 2024A Unified View of Group Fairness Tradeoffs Using Partial Information Decompositioncited by 3
  7. 2024In Search for Architectures and Loss Functions in Multi-Objective Reinforcement Learningcited by 3
  8. 2021The Sharpe predictor for fairness in machine learningcited by 2
  9. 2024Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimalitycited by 2
  10. 2024Best Practices for Responsible Machine Learning in Credit Scoringcited by 2
  11. 2025Aggregating Concepts of Fairness and Accuracy in Prediction Algorithmscited by 2
  12. 2025Audits Under Resource, Data, and Access Constraints: Scaling Laws For Less Discriminatory Alternativescited by 2
  13. 2023Superhuman Fairnesscited by 1
  14. 2024InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?cited by 1
  15. 2025A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systemscited by 1
  16. 2026Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systemscited by 1
  17. 2022Fair Classification via Transformer Neural Networks: Case Study of an Educational Domaincited by 0
  18. 2022Artificial Intelligence Nomenclature Identified From Delphi Study on Key Issues Related to Trust and Barriers to Adoption for Autonomous Systemscited by 0
  19. 2023FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairnesscited by 0
  20. 2023Search-Based Fairness Testing: An Overviewcited by 0
  21. 2023ATE-SG: Alternate Through the Epochs Stochastic Gradient for Multi-Task Neural Networkscited by 0
  22. 2024Evolutionary Multi-Objective Optimisation for Fairness-Aware Self Adjusting Memory Classifiers in Data Streamscited by 0
  23. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  24. 2025Controllable Pareto Trade-off between Fairness and Accuracycited by 0
  25. 2025A Unifying Human-Centered AI Fairness Frameworkcited by 0
  26. 2026Stochastic set-valued optimization and its application to robust learningcited by 0
  27. 2026Fair Classification with Efficient and Post-hoc Controllable Fairness-Accuracy Trade-offcited by 0