mlsanityData to 14 Oct 2026

Fairness Metrics: A Comparative Analysis

arXiv 2001.07864 ↗First posted 22 Jan 2020cs.CYcited by 45

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Abstract (arXiv)

Algorithmic fairness is receiving significant attention in the academic and broader literature due to the increasing use of predictive algorithms, including those based on artificial intelligence. One benefit of this trend is that algorithm designers and users have a growing set of fairness measures to choose from. However, this choice comes with the challenge of identifying how the different fairness measures relate to one another, as well as the extent to which they are compatible or mutually exclusive. We describe some of the most widely used fairness metrics using a common mathematical framework and present new results on the relationships among them. The results presented herein can help place both specialists and non-specialists in a better position to identify the metric best suited for their application and goals.

Key facts. Cited by 45 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Jan 2020.

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Citing papers per year: 2021: 5; 2022: 6; 2023: 9; 2024: 14; 2025: 4; 2026: 7.

45 papers.

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  1. 2021Fairness-aware Class Imbalanced Learningcited by 19
  2. 2022Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiencycited by 19
  3. 2021FairCal: Fairness Calibration for Face Verificationcited by 17
  4. 2023Uncertainty-based Fairness Measurescited by 15
  5. 2022Bias in Machine Learning Models Can Be Significantly Mitigated by Careful Training: Evidence from Neuroimaging Studiescited by 14
  6. 2022Mitigating Gender Bias in Face Recognition Using the von Mises-Fisher Mixture Modelcited by 12
  7. 2023FAIRER: Fairness as Decision Rationale Alignmentcited by 12
  8. 2024Can Machine Learning Assist in Diagnosis of Primary Immune Thrombocytopenia? A feasibility studycited by 8
  9. 2024Improving Large Language Model (LLM) fidelity through context-aware grounding: A systematic approach to reliability and veracitycited by 8
  10. 2022How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysiscited by 7
  11. 2023On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Modelcited by 7
  12. 2023Mining bias-target Alignment from Voronoi Cellscited by 5
33 more
  1. 2024X Hacking: The Threat of Misguided AutoMLcited by 5
  2. 2024Using Self-supervised Learning Can Improve Model Fairnesscited by 5
  3. 2024Understanding the Interplay of Scale, Data, and Bias in Language Models: A Case Study with BERTcited by 5
  4. 2023Retiring DeltaDP: New Distribution-Level Metrics for Demographic Paritycited by 4
  5. 2023Multi-Objective Population Based Trainingcited by 4
  6. 2021Evaluating the fairness of fine-tuning strategies in self-supervised learningcited by 3
  7. 2022Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifierscited by 3
  8. 2024On the Maximal Local Disparity of Fairness-Aware Classifierscited by 3
  9. 2024A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learningcited by 3
  10. 2025Gender Fairness of Machine Learning Algorithms for Pain Detectioncited by 3
  11. 2021Finding Representative Group Fairness Metrics Using Correlation Estimationscited by 2
  12. 2021There is an elephant in the room: Towards a critique on the use of fairness in biometricscited by 2
  13. 2023Gender Bias in Transformer Models: A comprehensive surveycited by 2
  14. 2024Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4cited by 2
  15. 2024Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factorscited by 2
  16. 2024The Disparate Benefits of Deep Ensemblescited by 2
  17. 2022Simultaneous Improvement of ML Model Fairness and Performance by Identifying Bias in Datacited by 1
  18. 2023Identifying Reasons for Bias: An Argumentation-Based Approachcited by 1
  19. 2024Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairnesscited by 1
  20. 2024Fairness-aware Multiobjective Evolutionary Learningcited by 1
  21. 2024Generating Fearful Images: Investigating Potential Emotional Biases in Image-Generation Modelscited by 1
  22. 2025FairMT: Fairness for Heterogeneous Multi-Task Learningcited by 1
  23. 2023Bias mitigation techniques in image classification: fair machine learning in human heritage collectionscited by 0
  24. 2024Generalizing Fairness to Generative Language Models via Reformulation of Non-discrimination Criteriacited by 0
  25. 2025LiLAW: Lightweight Learnable Adaptive Weighting to Learn Sample Difficulty & Improve Noisy Trainingcited by 0
  26. 2025Cost Efficient Fairness Audit Under Partial Feedbackcited by 0
  27. 2026Fairness in Healthcare Processes: A Quantitative Analysis of Decision Making in Triagecited by 0
  28. 2026Unravelling the (In)compatibility of Statistical-Parity and Equalized-Oddscited by 0
  29. 2026Unbiased Model Prediction Without Using Protected Attribute Informationcited by 0
  30. 2026Geometrical fairness in graph neural networkscited by 0
  31. 2026A fairness-aware extension of Stochastic Multicriteria Acceptability Analysis for rankingcited by 0
  32. 2026A Rank Graduation metric for Algorithmic fairnesscited by 0
  33. 2026Tolerance-Based Fairness Auditing: Violation Certification and Sensitivity Screeningcited by 0