# Fairness in Missing Data Imputation

arXiv: https://arxiv.org/abs/2110.12002 · HTML: https://mlsanity.com/paper/2110.12002/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 42 references that are in the map.
- First posted on arXiv 22 Oct 2021.

## Builds on (most cited first, 21 of 21)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness Beyond Disparate Treatment & Disparate Impact: Learning Classification without Disparate Mistreatment](https://mlsanity.com/paper/1610.08452/) (2016)
- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Gender Bias in Contextualized Word Embeddings](https://mlsanity.com/paper/1904.03310/) (2019)
- [Algorithmic Fairness](https://mlsanity.com/paper/2001.09784/) (2020)
- [The Frontiers of Fairness in Machine Learning](https://mlsanity.com/paper/1810.08810/) (2018)
- [Matrix Completion and Low-Rank SVD via Fast Alternating Least Squares](https://mlsanity.com/paper/1410.2596/) (2014)
- [Understanding the Origins of Bias in Word Embeddings](https://mlsanity.com/paper/1810.03611/) (2018)
- [Conditional Learning of Fair Representations](https://mlsanity.com/paper/1910.07162/) (2019)
- [Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction](https://mlsanity.com/paper/1807.00199/) (2018)
- [MisGAN: Learning from Incomplete Data with Generative Adversarial Networks](https://mlsanity.com/paper/1902.09599/) (2019)
- [Fairness in Learning-Based Sequential Decision Algorithms: A Survey](https://mlsanity.com/paper/2001.04861/) (2020)
- [The Importance of Modeling Data Missingness in Algorithmic Fairness: A Causal Perspective](https://mlsanity.com/paper/2012.11448/) (2020)
- [Fairness and Missing Values](https://mlsanity.com/paper/1905.12728/) (2019)
- [MIWAE: Deep Generative Modelling and Imputation of Incomplete Data](https://mlsanity.com/paper/1812.02633/) (2018)
- [Missing Features Reconstruction Using a Wasserstein Generative Adversarial Imputation Network](https://mlsanity.com/paper/2006.11783/) (2020)
- [Categorical EHR Imputation with Generative Adversarial Nets](https://mlsanity.com/paper/2108.01701/) (2021)

## Built on by (most cited first, 8 of 8)

- [Assessing Fairness in the Presence of Missing Data](https://mlsanity.com/paper/2112.04899/) (2021)
- [Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness](https://mlsanity.com/paper/2208.06648/) (2022)
- [Adapting Fairness Interventions to Missing Values](https://mlsanity.com/paper/2305.19429/) (2023)
- [What is Fair? Defining Fairness in Machine Learning for Health](https://mlsanity.com/paper/2406.09307/) (2024)
- [Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers](https://mlsanity.com/paper/2211.00783/) (2022)
- [Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation](https://mlsanity.com/paper/2409.07510/) (2024)
- [MISNN: Multiple Imputation via Semi-parametric Neural Networks](https://mlsanity.com/paper/2305.01794/) (2023)
- [The influence of missing data mechanisms and simple missing data handling techniques on fairness](https://mlsanity.com/paper/2503.07313/) (2025)

## 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.