mlsanityData to 7 Oct 2026

Assessing Fairness in the Presence of Missing Data

arXiv 2112.04899 ↗First posted 7 Dec 2021cs.LG cs.AIcited by 24

Not on the map. Why?

Abstract (arXiv)

Missing data are prevalent and present daunting challenges in real data analysis. While there is a growing body of literature on fairness in analysis of fully observed data, there has been little theoretical work on investigating fairness in analysis of incomplete data. In practice, a popular analytical approach for dealing with missing data is to use only the set of complete cases, i.e., observations with all features fully observed to train a prediction algorithm. However, depending on the missing data mechanism, the distribution of complete cases and the distribution of the complete data may be substantially different. When the goal is to develop a fair algorithm in the complete data domain where there are no missing values, an algorithm that is fair in the complete case domain may show disproportionate bias towards some marginalized groups in the complete data domain. To fill this significant gap, we study the problem of estimating fairness in the complete data domain for an arbitrary model evaluated merely using complete cases. We provide upper and lower bounds on the fairness estimation error and conduct numerical experiments to assess our theoretical results. Our work provides the first known theoretical results on fairness guarantee in analysis of incomplete data.

Key facts. Cited by 24 papers in the map (data through 7 Oct 2026). Builds on 19 of its 58 references that are in the map. First posted on arXiv 7 Dec 2021.

Builds on

19 of 58 references.

References · 19

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  3. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 349
  4. 2018Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systemscited by 234
  5. 2018Empirical Risk Minimization under Fairness Constraintscited by 226
  6. 2019Fair Regression: Quantitative Definitions and Reduction-based Algorithmscited by 168
  7. 2016Theoretical Analysis of Domain Adaptation with Optimal Transportcited by 105
  8. 2019Conditional Learning of Fair Representationscited by 78
  9. 2019MisGAN: Learning from Incomplete Data with Generative Adversarial Networkscited by 74
  10. 2018The age of secrecy and unfairness in recidivism predictioncited by 53
  11. 2019Robustness to Adversarial Perturbations in Learning from Incomplete Datacited by 49
  12. 2019On Learning Invariant Representation for Domain Adaptationcited by 38
7 more
  1. 2019Transfer of Machine Learning Fairness across Domainscited by 36
  2. 2019General Fair Empirical Risk Minimizationcited by 23
  3. 2020The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspectivecited by 17
  4. 2019Fairness and Missing Valuescited by 16
  5. 2016Exact Lower Bounds for the Agnostic Probably-Approximately-Correct (PAC) Machine Learning Modelcited by 8
  6. 2021Fairness in Missing Data Imputationcited by 8
  7. 2019Empirical Risk Minimization under Random Censorship: Theory and Practicecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 6; 2023: 6; 2024: 6; 2025: 4; 2026: 2.

24 papers.

Sort:

  1. 2023Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventionscited by 19
  2. 2023RedCore: Relative Advantage Aware Cross-modal Representation Learning for Missing Modalities with Imbalanced Missing Ratescited by 14
  3. 2022Bounding and Approximating Intersectional Fairness through Marginal Fairnesscited by 11
  4. 2024The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learningcited by 8
  5. 2022Consistent Range Approximation for Fair Predictive Modelingcited by 7
  6. 2023Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Toolscited by 7
  7. 2022Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairnesscited by 6
  8. 2023Adapting Fairness Interventions to Missing Valuescited by 5
  9. 2022Fairness in Forecasting of Observations of Linear Dynamical Systemscited by 4
  10. 2024The Impact of Differential Feature Under-reporting on Algorithmic Fairnesscited by 4
  11. 2025MCE: Towards a General Framework for Handling Missing Modalities under Imbalanced Missing Ratescited by 4
  12. 2022Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifierscited by 3
12 more
  1. 2023Algorithmic Recourse with Missing Valuescited by 2
  2. 2022I Prefer not to Say: Protecting User Consent in Models with Optional Personal Datacited by 1
  3. 2024A Note on Bias to Completecited by 1
  4. 2024Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputationcited by 1
  5. 2024FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairnesscited by 1
  6. 2026Even More Guarantees for Variational Inference in the Presence of Symmetriescited by 1
  7. 2023Fair Graph Machine Learning under Adversarial Missingness Processescited by 0
  8. 2024Fairness Risks for Group-conditionally Missing Demographicscited by 0
  9. 2025A Critical Review of Predominant Bias in Neural Networkscited by 0
  10. 2025Scalable Expectation Estimation with Subtractive Mixture Modelscited by 0
  11. 2025Cost Efficient Fairness Audit Under Partial Feedbackcited by 0
  12. 2026Failing on Bias Mitigation: A Case Study on the Challenges of Fairness in Government Datacited by 0