mlsanityData to 7 Oct 2026

What is Fair? Defining Fairness in Machine Learning for Health

arXiv 2406.09307 ↗First posted 13 Jun 2024cs.LG cs.CY stat.MLcited by 5

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Abstract (arXiv)

Ensuring that machine learning (ML) models are safe, effective, and equitable across all patients is critical for clinical decision-making and for preventing the amplification of existing health disparities. In this work, we examine how fairness is conceptualized in ML for health, including why ML models may lead to unfair decisions and how fairness has been measured in diverse real-world applications. We review commonly used fairness notions within group, individual, and causal-based frameworks. We also discuss the outlook for future research and highlight opportunities and challenges in operationalizing fairness in health-focused applications.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 47 of its 166 references that are in the map. First posted on arXiv 13 Jun 2024.

Builds on

47 of 166 references.

References · 47

  1. 2017On Calibration of Modern Neural Networkscited by 4,860
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 671
  4. 2018Mitigating Unwanted Biases with Adversarial Learningcited by 663
  5. 2017CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learningcited by 459
  6. 2020Fairness in Machine Learning: A Surveycited by 369
  7. 2017Fairness in Criminal Justice Risk Assessments: The State of the Artcited by 315
  8. 2019A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cyclecited by 192
  9. 2018Path-Specific Counterfactual Fairnesscited by 179
  10. 2021Reading Race: AI Recognises Patient's Racial Identity In Medical Imagescited by 155
  11. 2020CheXclusion: Fairness gaps in deep chest X-ray classifierscited by 153
  12. 2017Fair Inference On Outcomescited by 143
35 more
  1. 2021Disparities in Dermatology AI: Assessments Using Diverse Clinical Imagescited by 132
  2. 2019On the Apparent Conflict Between Individual and Group Fairnesscited by 126
  3. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  4. 2019Towards a Critical Race Methodology in Algorithmic Fairnesscited by 124
  5. 2017Fairness Testing: Testing Software for Discriminationcited by 123
  6. 2019Fair Generative Modeling via Weak Supervisioncited by 114
  7. 2018The Blessings of Multiple Causescited by 103
  8. 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
  9. 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
  10. 2023Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategiescited by 83
  11. 2016A causal framework for discovering and removing direct and indirect discriminationcited by 71
  12. 2018A Review of Challenges and Opportunities in Machine Learning for Healthcited by 67
  13. 2022Theories of "Gender" in NLP Bias Researchcited by 62
  14. 2019Counterfactual Risk Assessments, Evaluation, and Fairnesscited by 59
  15. 2018Fairness Through Computationally-Bounded Awarenesscited by 55
  16. 2020Two Simple Ways to Learn Individual Fairness Metrics from Datacited by 54
  17. 2020Survey on Causal-based Machine Learning Fairness Notionscited by 53
  18. 2018Causal Reasoning for Algorithmic Fairnesscited by 51
  19. 2019Metric Learning for Individual Fairnesscited by 51
  20. 2021Fairness for Unobserved Characteristics: Insights from Technological Impacts on Queer Communitiescited by 40
  21. 2022Causal Conceptions of Fairness and their Consequencescited by 38
  22. 2020Chasing Your Long Tails: Differentially Private Prediction in Health Care Settingscited by 37
  23. 2018Creating Fair Models of Atherosclerotic Cardiovascular Disease Riskcited by 23
  24. 2023The Possibility of Fairness: Revisiting the Impossibility Theorem in Practicecited by 18
  25. 2019Comment on "Blessings of Multiple Causes"cited by 16
  26. 2022Selection, Ignorability and Challenges With Causal Fairnesscited by 16
  27. 2020Beyond Individual and Group Fairnesscited by 13
  28. 2023Unmasking Bias in AI: A Systematic Review of Bias Detection and Mitigation Strategies in Electronic Health Record-based Modelscited by 9
  29. 2021Fairness in Missing Data Imputationcited by 8
  30. 2024On the (In)Compatibility between Group Fairness and Individual Fairnesscited by 6
  31. 2024Interventional Fairness on Partially Known Causal Graphs: A Constrained Optimization Approachcited by 6
  32. 2023Evaluate underdiagnosis and overdiagnosis bias of deep learning model on primary open-angle glaucoma diagnosis in under-served patient populationscited by 5
  33. 2024On the Inductive Biases of Demographic Parity-based Fair Learning Algorithmscited by 5
  34. 2022Causal Fairness Assessment of Treatment Allocation with Electronic Health Recordscited by 3
  35. 2024A New Paradigm for Counterfactual Reasoning in Fairness and Recoursecited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 4.

5 papers.

  1. 2025Fairmetrics: An R package for group fairness evaluationcited by 1
  2. 2026A pipeline for enabling path-specific causal fairness in observational health datacited by 0
  3. 2026Trade-offs Between Individual and Group Fairness in Machine Learning: A Comprehensive Reviewcited by 0
  4. 2026Multimodal Survival Modeling and Fairness-Aware Clinical Machine Learning for 5-Year Breast Cancer Risk Predictioncited by 0
  5. 2026FairBED: A Bayesian Experimental Design Approach to Gathering Fairer Datacited by 0