mlsanityData to 7 Oct 2026

The Fairness-Accuracy Pareto Front

arXiv 2008.10797 ↗First posted 25 Aug 2020cs.LG stat.MLcited by 26

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Abstract (arXiv)

Algorithmic fairness seeks to identify and correct sources of bias in machine learning algorithms. Confoundingly, ensuring fairness often comes at the cost of accuracy. We provide formal tools in this work for reconciling this fundamental tension in algorithm fairness. Specifically, we put to use the concept of Pareto optimality from multi-objective optimization and seek the fairness-accuracy Pareto front of a neural network classifier. We demonstrate that many existing algorithmic fairness methods are performing the so-called linear scalarization scheme which has severe limitations in recovering Pareto optimal solutions. We instead apply the Chebyshev scalarization scheme which is provably superior theoretically and no more computationally burdensome at recovering Pareto optimal solutions compared to the linear scheme.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Aug 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 4; 2023: 3; 2024: 5; 2025: 6; 2026: 5.

26 papers.

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  1. 2021Fair Normalizing Flowscited by 38
  2. 2025Learning to Optimize Multi-Objective Alignment Through Dynamic Reward Weightingcited by 23
  3. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  4. 2021Latent Space Smoothing for Individually Fair Representationscited by 18
  5. 2022Evaluating Proposed Fairness Models for Face Recognition Algorithmscited by 18
  6. 2024Fairness Without Harm: An Influence-Guided Active Sampling Approachcited by 12
  7. 2024Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approachcited by 6
  8. 2023Navigating Fairness Measures and Trade-Offscited by 5
  9. 2022Generalization In Multi-Objective Machine Learningcited by 4
  10. 2023Entropy-driven Fair and Effective Federated Learningcited by 4
  11. 2023The Limits of Fair Medical Imaging AI In The Wildcited by 4
  12. 2022Finding Pareto Trade-offs in Fair and Accurate Detection of Toxic Speechcited by 3
14 more
  1. 2025Fair Foundation Models for Medical Image Analysis: Challenges and Perspectivescited by 3
  2. 2021A Hybrid 2-stage Neural Optimization for Pareto Front Extractioncited by 2
  3. 2024Achievable Fairness on Your Data With Utility Guaranteescited by 2
  4. 2024Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimalitycited by 2
  5. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  6. 2025The Statistical Fairness-Accuracy Frontiercited by 2
  7. 2026Uncovering Cross-Objective Interference in Multi-Objective Alignmentcited by 2
  8. 2024Towards Harmless Rawlsian Fairness Regardless of Demographic Priorcited by 1
  9. 2025A Multi-Objective Evaluation Framework for Analyzing Utility-Fairness Trade-Offs in Machine Learning Systemscited by 1
  10. 2026Fairness vs Performance: Characterizing the Pareto Frontier of Algorithmic Decision Systemscited by 1
  11. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  12. 2026First-See-Then-Design: A Multi-Stakeholder View for Optimal Performance-Fairness Trade-Offscited by 0
  13. 2026The Pok'emon Theorem and other Fairness Impossibility Resultscited by 0
  14. 2026GESD: Beyond Outcome-Oriented Fairnesscited by 0