# Assessing Fairness in the Presence of Missing Data arXiv: https://arxiv.org/abs/2112.04899 ยท HTML: https://mlsanity.com/paper/2112.04899/ ## Key facts - Cited by 24 papers in the map (data through 14 Oct 2026). - Builds on 19 of its 58 references that are in the map. - First posted on arXiv 7 Dec 2021. ## Builds on (most cited first, 19 of 19) - [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016) - [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) - [Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems](https://mlsanity.com/paper/1805.04508/) (2018) - [Empirical Risk Minimization under Fairness Constraints](https://mlsanity.com/paper/1802.08626/) (2018) - [Fair Regression: Quantitative Definitions and Reduction-based Algorithms](https://mlsanity.com/paper/1905.12843/) (2019) - [Theoretical Analysis of Domain Adaptation with Optimal Transport](https://mlsanity.com/paper/1610.04420/) (2016) - [Conditional Learning of Fair Representations](https://mlsanity.com/paper/1910.07162/) (2019) - [MisGAN: Learning from Incomplete Data with Generative Adversarial Networks](https://mlsanity.com/paper/1902.09599/) (2019) - [The age of secrecy and unfairness in recidivism prediction](https://mlsanity.com/paper/1811.00731/) (2018) - [Robustness to Adversarial Perturbations in Learning from Incomplete Data](https://mlsanity.com/paper/1905.13021/) (2019) - [On Learning Invariant Representation for Domain Adaptation](https://mlsanity.com/paper/1901.09453/) (2019) - [Transfer of Machine Learning Fairness across Domains](https://mlsanity.com/paper/1906.09688/) (2019) - [General Fair Empirical Risk Minimization](https://mlsanity.com/paper/1901.10080/) (2019) - [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) - [Exact Lower Bounds for the Agnostic Probably-Approximately-Correct (PAC) Machine Learning Model](https://mlsanity.com/paper/1606.08920/) (2016) - [Fairness in Missing Data Imputation](https://mlsanity.com/paper/2110.12002/) (2021) - [Empirical Risk Minimization under Random Censorship: Theory and Practice](https://mlsanity.com/paper/1906.01908/) (2019) ## Built on by (most cited first, 24 of 24) - [Aleatoric and Epistemic Discrimination: Fundamental Limits of Fairness Interventions](https://mlsanity.com/paper/2301.11781/) (2023) - [RedCore: Relative Advantage Aware Cross-modal Representation Learning for Missing Modalities with Imbalanced Missing Rates](https://mlsanity.com/paper/2312.10386/) (2023) - [Bounding and Approximating Intersectional Fairness through Marginal Fairness](https://mlsanity.com/paper/2206.05828/) (2022) - [The Fragility of Fairness: Causal Sensitivity Analysis for Fair Machine Learning](https://mlsanity.com/paper/2410.09600/) (2024) - [Consistent Range Approximation for Fair Predictive Modeling](https://mlsanity.com/paper/2212.10839/) (2022) - [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023) - [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) - [Fairness in Forecasting of Observations of Linear Dynamical Systems](https://mlsanity.com/paper/2209.05274/) (2022) - [The Impact of Differential Feature Under-reporting on Algorithmic Fairness](https://mlsanity.com/paper/2401.08788/) (2024) - [MCE: Towards a General Framework for Handling Missing Modalities under Imbalanced Missing Rates](https://mlsanity.com/paper/2510.10534/) (2025) - [Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers](https://mlsanity.com/paper/2211.00783/) (2022) - [Algorithmic Recourse with Missing Values](https://mlsanity.com/paper/2304.14606/) (2023) - [FairlyUncertain: A Comprehensive Benchmark of Uncertainty in Algorithmic Fairness](https://mlsanity.com/paper/2410.02005/) (2024) - [I Prefer not to Say: Protecting User Consent in Models with Optional Personal Data](https://mlsanity.com/paper/2210.13954/) (2022) - [A Note on Bias to Complete](https://mlsanity.com/paper/2402.11710/) (2024) - [Still More Shades of Null: An Evaluation Suite for Responsible Missing Value Imputation](https://mlsanity.com/paper/2409.07510/) (2024) - [Even More Guarantees for Variational Inference in the Presence of Symmetries](https://mlsanity.com/paper/2604.21407/) (2026) - [Fair Graph Machine Learning under Adversarial Missingness Processes](https://mlsanity.com/paper/2311.01591/) (2023) - [Fairness Risks for Group-conditionally Missing Demographics](https://mlsanity.com/paper/2402.13393/) (2024) - [A Critical Review of Predominant Bias in Neural Networks](https://mlsanity.com/paper/2502.11031/) (2025) - [Scalable Expectation Estimation with Subtractive Mixture Models](https://mlsanity.com/paper/2503.21346/) (2025) - [Cost Efficient Fairness Audit Under Partial Feedback](https://mlsanity.com/paper/2510.03734/) (2025) - [Failing on Bias Mitigation: A Case Study on the Challenges of Fairness in Government Data](https://mlsanity.com/paper/2601.17054/) (2026) ## 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.