  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.
