Fairness in Missing Data Imputation
This addresses fairness issues in data-driven decision-making for practitioners handling missing data, but it is incremental as it identifies a problem without proposing a new solution.
The paper tackled the problem of fairness in missing data imputation, demonstrating that unfairness widely exists across sensitive groups in three datasets and may be associated with multiple factors.
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.