mlsanityData to 7 Oct 2026

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

arXiv 2206.04101 ↗First posted 8 Jun 2022cs.LG cs.CYcited by 18

Not on the map. Why?

Abstract (arXiv)

Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addressed. In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We also consider fairness inquiries from a dynamic perspective, and further consider the long-term impact that is induced by current prediction and decision. In light of the differences in the characterized fairness, we present a flowchart that encompasses implicit assumptions and expected outcomes of different types of fairness inquiries on the data generating process, on the predicted outcome, and on the induced impact, respectively. This paper demonstrates the importance of matching the mission (which kind of fairness one would like to enforce) and the means (which spectrum of fairness analysis is of interest, what is the appropriate analyzing scheme) to fulfill the intended purpose.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 122 of its 245 references that are in the map. First posted on arXiv 8 Jun 2022.

Builds on

122 of 245 references.

References · 122

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  4. 2017Counterfactual Fairnesscited by 802
  5. 2015Deep Learning and the Information Bottleneck Principlecited by 793
  6. 2014Certifying and removing disparate impactcited by 715
  7. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  8. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  9. 2018A Reductions Approach to Fair Classificationcited by 585
  10. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
  11. 2017Algorithmic decision making and the cost of fairnesscited by 402
  12. 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
38 more
  1. 2020Fairness in Machine Learning: A Surveycited by 369
  2. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  3. 2018Fairness Without Demographics in Repeated Loss Minimizationcited by 363
  4. 2018Learning Adversarially Fair and Transferable Representationscited by 353
  5. 2018Actionable Recourse in Linear Classificationcited by 327
  6. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  7. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  8. 2019Flexibly Fair Representation Learning by Disentanglementcited by 196
  9. 2017Avoiding Discrimination through Causal Reasoningcited by 191
  10. 2018Fairness of Exposure in Rankingscited by 189
  11. 2018Delayed Impact of Fair Machine Learningcited by 183
  12. 2018Path-Specific Counterfactual Fairnesscited by 179
  13. 2016Fairness in Learning: Classic and Contextual Banditscited by 167
  14. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  15. 2017Fairness in Machine Learning: Lessons from Political Philosophycited by 148
  16. 2017A Convex Framework for Fair Regressioncited by 147
  17. 2017Fair Inference On Outcomescited by 143
  18. 2016Foundations of Structural Causal Models with Cycles and Latent Variablescited by 134
  19. 2019Towards Realistic Individual Recourse and Actionable Explanations in Black-Box Decision Making Systemscited by 128
  20. 2017Strategic Classification from Revealed Preferencescited by 126
  21. 2018An Intersectional Definition of Fairnesscited by 126
  22. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  23. 2019The Hidden Assumptions Behind Counterfactual Explanations and Principal Reasonscited by 115
  24. 2021Fair Mixup: Fairness via Interpolationcited by 109
  25. 2018The Social Cost of Strategic Classificationcited by 105
  26. 2017Runaway Feedback Loops in Predictive Policingcited by 97
  27. 2018A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indicescited by 97
  28. 2018Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobservedcited by 94
  29. 2013Discrimination in Online Ad Deliverycited by 93
  30. 2016Fairness in Reinforcement Learningcited by 93
  31. 2014Automated Experiments on Ad Privacy Settings: A Tale of Opacity, Choice, and Discriminationcited by 83
  32. 2018Learning Controllable Fair Representationscited by 83
  33. 2018How Do Classifiers Induce Agents To Invest Effort Strategically?cited by 82
  34. 2018An Empirical Study of Rich Subgroup Fairness for Machine Learningcited by 81
  35. 2019Conditional Learning of Fair Representationscited by 78
  36. 2020Robust Optimization for Fairness with Noisy Protected Groupscited by 78
  37. 2019Achieving Fairness in the Stochastic Multi-armed Bandit Problemcited by 77
  38. 2021Towards Long-term Fairness in Recommendationcited by 77

50 most cited of 122 All 122 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 4; 2024: 10; 2025: 2; 2026: 2.

18 papers.

Sort:

  1. 2024Prompting Fairness: Integrating Causality to Debias Large Language Modelscited by 20
  2. 2023Causal Adversarial Perturbations for Individual Fairness and Robustness in Heterogeneous Data Spacescited by 4
  3. 2023Directed Cyclic Graph for Causal Discovery from Multivariate Functional Datacited by 4
  4. 2024Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learningcited by 4
  5. 2024Procedural Fairness in Machine Learningcited by 4
  6. 2024Long-Term Fairness Inquiries and Pursuits in Machine Learning: A Survey of Notions, Methods, and Challengescited by 4
  7. 2023Procedural Fairness Through Decoupling Objectionable Data Generating Componentscited by 3
  8. 2024A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learningcited by 3
  9. 2024Counterfactual Fairness through Transforming Data Orthogonal to Biascited by 2
  10. 2024Measuring Model-Induced Discrimination via Efficient Fairness Approximationcited by 2
  11. 2024Outlier Detection Bias Busted: Understanding Sources of Algorithmic Bias through Data-centric Factorscited by 2
  12. 2023Assessing Electricity Service Unfairness with Transfer Counterfactual Learningcited by 1
6 more
  1. 2024Long-Term Fair Decision Making through Deep Generative Modelscited by 1
  2. 2026Happy Young Women, Grumpy Old Men? Emotion-Driven Demographic Biases in Synthetic Face Generationcited by 1
  3. 2024Beyond RMSE and MAE: Introducing EAUC to unmask hidden bias and unfairness in dyadic regression modelscited by 0
  4. 2025Position: Beyond Sensitive Attributes, ML Fairness Should Quantify Structural Injustice via Social Determinantscited by 0
  5. 2025"I think this is fair": Uncovering the Complexities of Stakeholder Decision-Making in AI Fairness Assessmentcited by 0
  6. 2026Whose fairness? Structural concentration in AI bias researchcited by 0