mlsanityData to 7 Oct 2026

Algorithmic Fairness

arXiv 2001.09784 ↗First posted 21 Jan 2020cs.CY cs.AI cs.LGcited by 143

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Abstract (arXiv)

An increasing number of decisions regarding the daily lives of human beings are being controlled by artificial intelligence (AI) algorithms in spheres ranging from healthcare, transportation, and education to college admissions, recruitment, provision of loans and many more realms. Since they now touch on many aspects of our lives, it is crucial to develop AI algorithms that are not only accurate but also objective and fair. Recent studies have shown that algorithmic decision-making may be inherently prone to unfairness, even when there is no intention for it. This paper presents an overview of the main concepts of identifying, measuring and improving algorithmic fairness when using AI algorithms. The paper begins by discussing the causes of algorithmic bias and unfairness and the common definitions and measures for fairness. Fairness-enhancing mechanisms are then reviewed and divided into pre-process, in-process and post-process mechanisms. A comprehensive comparison of the mechanisms is then conducted, towards a better understanding of which mechanisms should be used in different scenarios. The paper then describes the most commonly used fairness-related datasets in this field. Finally, the paper ends by reviewing several emerging research sub-fields of algorithmic fairness.

Key facts. Cited by 143 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Jan 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 28; 2022: 34; 2023: 29; 2024: 23; 2025: 15; 2026: 14.

143 papers.

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  1. 2021A survey on datasets for fairness-aware machine learningcited by 157
  2. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  3. 2024Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systemscited by 94
  4. 2024Trustworthiness in Retrieval-Augmented Generation Systems: A Surveycited by 83
  5. 2022Fairness in Graph Mining: A Surveycited by 76
  6. 2023Explore Spurious Correlations at the Concept Level in Language Models for Text Classificationcited by 48
  7. 2021TabFairGAN: Fair Tabular Data Generation with Generative Adversarial Networkscited by 34
  8. 2021Two-sided fairness in rankings via Lorenz dominancecited by 28
  9. 2021Training Fair Models in Federated Learning without Data Privacy Infringementcited by 26
  10. 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
  11. 2023Auditing Fairness by Bettingcited by 24
  12. 2021Testing Group Fairness via Optimal Transport Projectionscited by 23
38 more
  1. 2022Debiasing Methods for Fairer Neural Models in Vision and Language Research: A Surveycited by 22
  2. 2021Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systemscited by 20
  3. 2022Learning fair representation with a parametric integral probability metriccited by 20
  4. 2022FairNeuron: Improving Deep Neural Network Fairness with Adversary Games on Selective Neuronscited by 20
  5. 2022Enforcing Group Fairness in Algorithmic Decision Making: Utility Maximization Under Sufficiencycited by 19
  6. 2024Root Cause Analysis of Outliers with Missing Structural Knowledgecited by 19
  7. 2022Algorithmic Fairness in Business Analytics: Directions for Research and Practicecited by 18
  8. 2022Interpreting Unfairness in Graph Neural Networks via Training Node Attributioncited by 18
  9. 2021Modeling Techniques for Machine Learning Fairness: A Surveycited by 17
  10. 2022The worst of both worlds: A comparative analysis of errors in learning from data in psychology and machine learningcited by 17
  11. 2022CIRCLe: Color Invariant Representation Learning for Unbiased Classification of Skin Lesionscited by 17
  12. 2023A Critical Survey on Fairness Benefits of Explainable AIcited by 16
  13. 2021Towards Equity and Algorithmic Fairness in Student Grade Predictioncited by 13
  14. 2021Achieving Fairness with a Simple Ridge Penaltycited by 13
  15. 2022PrivFairFL: Privacy-Preserving Group Fairness in Federated Learningcited by 13
  16. 2024The Pursuit of Fairness in Artificial Intelligence Models: A Surveycited by 13
  17. 2022The European AI Liability Directives -- Critique of a Half-Hearted Approach and Lessons for the Futurecited by 12
  18. 2023Metrics for Dataset Demographic Bias: A Case Study on Facial Expression Recognitioncited by 12
  19. 2023A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systemscited by 12
  20. 2024Establishing and Evaluating Trustworthy AI: Overview and Research Challengescited by 12
  21. 2021On the Fairness of Machine-Assisted Human Decisionscited by 10
  22. 2022Fairness and Bias in Robot Learningcited by 10
  23. 2022FAIR-FATE: Fair Federated Learning with Momentumcited by 10
  24. 2024Lazy Data Practices Harm Fairness Researchcited by 10
  25. 2022Assessing Demographic Bias Transfer from Dataset to Model: A Case Study in Facial Expression Recognitioncited by 9
  26. 2024Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibriumcited by 9
  27. 2021An Empirical Comparison of Bias Reduction Methods on Real-World Problems in High-Stakes Policy Settingscited by 8
  28. 2021How Costly is Noise? Data and Disparities in Consumer Creditcited by 8
  29. 2021Achieving Model Fairness in Vertical Federated Learningcited by 8
  30. 2021Fairness in Missing Data Imputationcited by 8
  31. 2022Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learningcited by 8
  32. 2023Vertical Federated Learning: Taxonomies, Threats, and Prospectscited by 8
  33. 2023Understanding Fairness Surrogate Functions in Algorithmic Fairnesscited by 8
  34. 2023Designing Long-term Group Fair Policies in Dynamical Systemscited by 8
  35. 2023Is Your Model "MADD"? A Novel Metric to Evaluate Algorithmic Fairness for Predictive Student Modelscited by 7
  36. 2022When Personalization Harms: Reconsidering the Use of Group Attributes in Predictioncited by 6
  37. 2022Through a fair looking-glass: mitigating bias in image datasetscited by 6
  38. 2023Integrating Psychometrics and Computing Perspectives on Bias and Fairness in Affective Computing: A Case Study of Automated Video Interviewscited by 6

50 most cited of 143 All 143 (JSON)