mlsanityData to 7 Oct 2026

Fairness in Missing Data Imputation

arXiv 2110.12002 ↗First posted 22 Oct 2021cs.LG cs.AI stat.MEcited by 8

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Abstract (arXiv)

Missing data are ubiquitous in the era of big data and, if inadequately handled, are known to lead to biased findings and have deleterious impact on data-driven decision makings. To mitigate its impact, many missing value imputation methods have been developed. However, the fairness of these imputation methods across sensitive groups has not been studied. In this paper, we conduct the first known research on fairness of missing data imputation. By studying the performance of imputation methods in three commonly used datasets, we demonstrate that unfairness of missing value imputation widely exists and may be associated with multiple factors. Our results suggest that, in practice, a careful investigation of related factors can provide valuable insights on mitigating unfairness associated with missing data imputation.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 21 of its 42 references that are in the map. First posted on arXiv 22 Oct 2021.

Builds on

21 of 42 references.

References · 21

  1. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  2. 2016Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddingscited by 1,304
  3. 2018Gender Bias in Coreference Resolution: Evaluation and Debiasing Methodscited by 657
  4. 2017Algorithmic decision making and the cost of fairnesscited by 402
  5. 2016Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatmentcited by 368
  6. 2018GAIN: Missing Data Imputation using Generative Adversarial Netscited by 349
  7. 2019Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Themcited by 268
  8. 2019Gender Bias in Contextualized Word Embeddingscited by 203
  9. 2020Algorithmic Fairnesscited by 143
  10. 2018The Frontiers of Fairness in Machine Learningcited by 139
  11. 2014Matrix Completion and Low-Rank SVD via Fast Alternating Least Squarescited by 104
  12. 2018Understanding the Origins of Bias in Word Embeddingscited by 90
9 more
  1. 2019Conditional Learning of Fair Representationscited by 78
  2. 2018Achieving Fairness through Adversarial Learning: an Application to Recidivism Predictioncited by 77
  3. 2019MisGAN: Learning from Incomplete Data with Generative Adversarial Networkscited by 74
  4. 2020Fairness in Learning-Based Sequential Decision Algorithms: A Surveycited by 35
  5. 2020The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspectivecited by 17
  6. 2019Fairness and Missing Valuescited by 16
  7. 2018MIWAE: Deep Generative Modelling and Imputation of Incomplete Datacited by 14
  8. 2020Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Networkcited by 5
  9. 2021Categorical EHR Imputation with Generative Adversarial Netscited by 4

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 2; 2024: 2; 2025: 1.

8 papers.

  1. 2021Assessing Fairness in the Presence of Missing Datacited by 24
  2. 2022Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairnesscited by 6
  3. 2023Adapting Fairness Interventions to Missing Valuescited by 5
  4. 2024What is Fair? Defining Fairness in Machine Learning for Healthcited by 5
  5. 2022Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifierscited by 3
  6. 2024Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputationcited by 1
  7. 2023MISNN: Multiple Imputation via Semi-parametric Neural Networkscited by 0
  8. 2025The influence of missing data mechanisms and simple missing data handling techniques on fairnesscited by 0