mlsanityData to 7 Oct 2026

Longitudinal Fairness with Censorship

arXiv 2203.16024 ↗First posted 30 Mar 2022cs.LG cs.AI cs.CYcited by 21

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Abstract (arXiv)

Recent works in artificial intelligence fairness attempt to mitigate discrimination by proposing constrained optimization programs that achieve parity for some fairness statistic. Most assume availability of the class label, which is impractical in many real-world applications such as precision medicine, actuarial analysis and recidivism prediction. Here we consider fairness in longitudinal right-censored environments, where the time to event might be unknown, resulting in censorship of the class label and inapplicability of existing fairness studies. We devise applicable fairness measures, propose a debiasing algorithm, and provide necessary theoretical constructs to bridge fairness with and without censorship for these important and socially-sensitive tasks. Our experiments on four censored datasets confirm the utility of our approach.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 24 of its 67 references that are in the map. First posted on arXiv 30 Mar 2022.

Builds on

24 of 67 references.

References · 24

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 354
  4. 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 173
  5. 2017Data Decisions and Theoretical Implications when Adversarially Learning Fair Representationscited by 165
  6. 2021A survey on datasets for fairness-aware machine learningcited by 157
  7. 2017Machine Learning for Survival Analysis: A Surveycited by 112
  8. 2020Ethical Machine Learning in Health Carecited by 112
  9. 2016A Confidence-Based Approach for Balancing Fairness and Accuracycited by 73
  10. 2021Mitigating Political Bias in Language Models Through Reinforced Calibrationcited by 73
  11. 2019Putting Fairness Principles into Practice: Challenges, Metrics, and Improvementscited by 70
  12. 2018Effective Ways to Build and Evaluate Individual Survival Distributionscited by 64
12 more
  1. 2019FAHT: An Adaptive Fairness-aware Decision Tree Classifiercited by 63
  2. 2021FARF: A Fair and Adaptive Random Forests Classifiercited by 37
  3. 2020Disentangled Dynamic Graph Deep Generationcited by 27
  4. 2018A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classificationcited by 22
  5. 2020LiFT: A Scalable Framework for Measuring Fairness in ML Applicationscited by 22
  6. 2022Fairness Amidst Non-IID Graph Data: A Literature Reviewcited by 20
  7. 2020Using Machine Learning to Automate Mammogram Images Analysiscited by 15
  8. 2021LSTM Based Sentiment Analysis for Cryptocurrency Predictioncited by 14
  9. 2021Cognitive Visual Commonsense Reasoning Using Dynamic Working Memorycited by 14
  10. 2020Fair and Truthful Mechanisms for Dichotomous Valuationscited by 12
  11. 2020Equitable Allocation of Healthcare Resources with Fair Cox Modelscited by 6
  12. 2020Fairness-Aware Learning with Prejudice Free Representationscited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 3; 2024: 6; 2025: 7; 2026: 1.

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  1. 2022Bias Mitigation for Machine Learning Classifiers: A Comprehensive Surveycited by 125
  2. 2024History, Development, and Principles of Large Language Models-An Introductory Surveycited by 30
  3. 2023Fair Decision-making Under Uncertaintycited by 15
  4. 2024FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applicationscited by 15
  5. 2022Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographicscited by 14
  6. 2023Towards Fair Machine Learning Software: Understanding and Addressing Model Bias Through Counterfactual Thinkingcited by 14
  7. 2024Fairness in Survival Analysis with Distributionally Robust Optimizationcited by 13
  8. 2023Preventing Discriminatory Decision-making in Evolving Data Streamscited by 12
  9. 2024Fairness Definitions in Language Models Explainedcited by 10
  10. 2025Digital Forensics in the Age of Large Language Modelscited by 10
  11. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applicationscited by 5
  12. 2022Flexible Group Fairness Metrics for Survival Analysiscited by 4
9 more
  1. 2024AI-Driven Healthcare: A Review on Ensuring Fairness and Mitigating Biascited by 3
  2. 2025AMCR: A Framework for Assessing and Mitigating Copyright Risks in Generative Modelscited by 3
  3. 2025Towards Transparent AI: A Survey on Explainable Language Modelscited by 3
  4. 2025Critical Appraisal of Fairness Metrics in Clinical Predictive AIcited by 2
  5. 2025Competing Risks: Impact on Risk Estimation and Algorithmic Fairnesscited by 2
  6. 2024Uncertain Boundaries: Multidisciplinary Approaches to Copyright Issues in Generative AIcited by 1
  7. 2025Fairness-Aware Graph Representation Learning with Limited Demographic Informationcited by 1
  8. 2022Attention Mechanism based Cognition-level Scene Understandingcited by 0
  9. 2026Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysiscited by 0