# Adapting Fairness Interventions to Missing Values

arXiv: https://arxiv.org/abs/2305.19429 · HTML: https://mlsanity.com/paper/2305.19429/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 62 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 26 of 26)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Empirical Risk Minimization under Fairness Constraints](https://mlsanity.com/paper/1802.08626/) (2018)
- [Multiaccuracy: Black-Box Post-Processing for Fairness in Classification](https://mlsanity.com/paper/1805.12317/) (2018)
- [Predictive Multiplicity in Classification](https://mlsanity.com/paper/1909.06677/) (2019)
- [Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees](https://mlsanity.com/paper/1806.06055/) (2018)
- [Identifying and Correcting Label Bias in Machine Learning](https://mlsanity.com/paper/1901.04966/) (2019)
- [Assessing Algorithmic Fairness with Unobserved Protected Class Using Data Combination](https://mlsanity.com/paper/1906.00285/) (2019)
- [Supersparse Linear Integer Models for Optimized Medical Scoring Systems](https://mlsanity.com/paper/1502.04269/) (2015)
- [What's a good imputation to predict with missing values?](https://mlsanity.com/paper/2106.00311/) (2021)
- [Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification](https://mlsanity.com/paper/1906.05082/) (2019)
- [On the consistency of supervised learning with missing values](https://mlsanity.com/paper/1902.06931/) (2019)
- [Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference](https://mlsanity.com/paper/2010.09851/) (2020)
- [Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values](https://mlsanity.com/paper/2109.10431/) (2021)
- [Assessing Fairness in the Presence of Missing Data](https://mlsanity.com/paper/2112.04899/) (2021)
- [Benchmarking missing-values approaches for predictive models on health databases](https://mlsanity.com/paper/2202.10580/) (2022)
- [Linear predictor on linearly-generated data with missing values: non consistency and solutions](https://mlsanity.com/paper/2002.00658/) (2020)
- [On Fairness, Diversity and Randomness in Algorithmic Decision Making](https://mlsanity.com/paper/1706.10208/) (2017)
- [FairPrep: Promoting Data to a First-Class Citizen in Studies on Fairness-Enhancing Interventions](https://mlsanity.com/paper/1911.12587/) (2019)
- [Fairness-aware Model-agnostic Positive and Unlabeled Learning](https://mlsanity.com/paper/2206.09346/) (2022)
- [Fairness in Missing Data Imputation](https://mlsanity.com/paper/2110.12002/) (2021)
- [Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers](https://mlsanity.com/paper/2211.00783/) (2022)
- [Bootstrapping and Multiple Imputation Ensemble Approaches for Missing Data](https://mlsanity.com/paper/1802.00154/) (2018)
- [Group Fairness with Uncertainty in Sensitive Attributes](https://mlsanity.com/paper/2302.08077/) (2023)

## Built on by (most cited first, 5 of 5)

- [Exploring the Impact of Dataset Bias on Dataset Distillation](https://mlsanity.com/paper/2403.16028/) (2024)
- [Addressing Missing Data Issue for Diffusion-based Recommendation](https://mlsanity.com/paper/2505.12283/) (2025)
- [On the Maximal Local Disparity of Fairness-Aware Classifiers](https://mlsanity.com/paper/2406.03255/) (2024)
- [Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation](https://mlsanity.com/paper/2409.07510/) (2024)
- [Fair Graph Machine Learning under Adversarial Missingness Processes](https://mlsanity.com/paper/2311.01591/) (2023)

## Abstract (arXiv)

Missing values in real-world data pose a significant and unique challenge to algorithmic fairness. Different demographic groups may be unequally affected by missing data, and the standard procedure for handling missing values where first data is imputed, then the imputed data is used for classification -- a procedure referred to as "impute-then-classify" -- can exacerbate discrimination. In this paper, we analyze how missing values affect algorithmic fairness. We first prove that training a classifier from imputed data can significantly worsen the achievable values of group fairness and average accuracy. This is because imputing data results in the loss of the missing pattern of the data, which often conveys information about the predictive label. We present scalable and adaptive algorithms for fair classification with missing values. These algorithms can be combined with any preexisting fairness-intervention algorithm to handle all possible missing patterns while preserving information encoded within the missing patterns. Numerical experiments with state-of-the-art fairness interventions demonstrate that our adaptive algorithms consistently achieve higher fairness and accuracy than impute-then-classify across different datasets.