mlsanityData to 7 Oct 2026

On the relation between accuracy and fairness in binary classification

arXiv 1505.05723 ↗First posted 21 May 2015cs.LG cs.AIcited by 47

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Abstract (arXiv)

Our study revisits the problem of accuracy-fairness tradeoff in binary classification. We argue that comparison of non-discriminatory classifiers needs to account for different rates of positive predictions, otherwise conclusions about performance may be misleading, because accuracy and discrimination of naive baselines on the same dataset vary with different rates of positive predictions. We provide methodological recommendations for sound comparison of non-discriminatory classifiers, and present a brief theoretical and empirical analysis of tradeoffs between accuracy and non-discrimination.

Key facts. Cited by 47 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 May 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 11; 2023: 7; 2024: 4; 2025: 7; 2026: 4.

47 papers.

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  1. 2021A survey on datasets for fairness-aware machine learningcited by 157
  2. 2022Perturbation Augmentation for Fairer NLPcited by 51
  3. 2021Understanding and Mitigating Annotation Bias in Facial Expression Recognitioncited by 50
  4. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  5. 2021Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learningcited by 34
  6. 2021Evaluating Fairness of Machine Learning Models Under Uncertain and Incomplete Informationcited by 33
  7. 2021Fairness in Ranking under Uncertaintycited by 27
  8. 2023Fair Machine Unlearning: Data Removal while Mitigating Disparitiescited by 25
  9. 2021Characterizing the risk of fairwashingcited by 21
  10. 2022Counterfactual Fairness with Partially Known Causal Graphcited by 20
  11. 2021A Framework for Fairness: A Systematic Review of Existing Fair AI Solutionscited by 18
  12. 2021Algorithmic Fairness Verification with Graphical Modelscited by 16
35 more
  1. 2022Counterfactual Fairness Is Basically Demographic Paritycited by 15
  2. 2021Disentangled Representations from Non-Disentangled Modelscited by 13
  3. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  4. 2023How Spurious Features Are Memorized: Precise Analysis for Random and NTK Featurescited by 11
  5. 2022To the Fairness Frontier and Beyond: Identifying, Quantifying, and Optimizing the Fairness-Accuracy Pareto Frontiercited by 10
  6. 2022Group Meritocratic Fairness in Linear Contextual Banditscited by 9
  7. 2022Survey on Fairness Notions and Related Tensionscited by 9
  8. 2025Spurious Correlations in High Dimensional Regression: The Roles of Regularization, Simplicity Bias and Over-Parameterizationcited by 9
  9. 2021An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settingscited by 8
  10. 2021Costs and Benefits of Fair Regressioncited by 8
  11. 2022How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysiscited by 7
  12. 2024Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approachcited by 6
  13. 2021Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problemcited by 5
  14. 2022Evaluation of group fairness measures in student performance prediction problemscited by 5
  15. 2025Overcoming Fairness Trade-offs via Pre-processing: A Causal Perspectivecited by 5
  16. 2022Fairness in Forecasting of Observations of Linear Dynamical Systemscited by 4
  17. 2024A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learningcited by 3
  18. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  19. 2025Aggregating Concepts of Fairness and Accuracy in Prediction Algorithmscited by 2
  20. 2023Bias Mitigation Methods for Binary Classification Decision-Making Systems: Survey and Recommendationscited by 1
  21. 2023A Counterfactual Fair Model for Longitudinal Electronic Health Records via Deconfoundercited by 1
  22. 2023Rethinking Algorithmic Fairness for Human-AI Collaborationcited by 1
  23. 2023Fair Feature Importance Scores for Interpreting Tree-Based Methods and Surrogatescited by 1
  24. 2024LLM-Assisted Content Conditional Debiasing for Fair Text Embeddingcited by 1
  25. 2025Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanationscited by 1
  26. 2026Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solutioncited by 1
  27. 2021Coping with Mistreatment in Fair Algorithmscited by 0
  28. 2021CONFAIR: Configurable and Interpretable Algorithmic Fairnesscited by 0
  29. 2022Causal Intervention for Fairness in Multi-behavior Recommendationcited by 0
  30. 2023FairPilot: An Explorative System for Hyperparameter Tuning through the Lens of Fairnesscited by 0
  31. 2025You Only Debias Once: Towards Flexible Accuracy-Fairness Trade-offs at Inference Timecited by 0
  32. 2025Algorithmic Fairness: Not a Purely Technical but Socio-Technical Propertycited by 0
  33. 2026Unravelling the (In)compatibility of Statistical-Parity and Equalized-Oddscited by 0
  34. 2026Investigating Data Interventions for Subgroup Fairness: An ICU Case Studycited by 0
  35. 2026Controlling for Omitted Variable Bias in Deep Neural Networkscited by 0