Fairness in Missing Data Imputation

Abstract

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.

Assessing Fairness in the Presence of Missing Data

“…Several existing works [18,44,48,39,30,21] are related to our work, but there are a number of fundamental differences between these works and ours. [18,44,48] investigated the impact of missing data on the fairness of downstream prediction models.…”

“…Several existing works [18,44,48,39,30,21] are related to our work, but there are a number of fundamental differences between these works and ours. [18,44,48] investigated the impact of missing data on the fairness of downstream prediction models. [39] investigated fairness across different domains and provided an upper bound of fairness in the target domain given fairness estimate in the source domain.…”