Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers

Analysis of the fairness of machine learning (ML) algorithms has attracted many researchers' interest. Several studies have shown that ML methods produce a bias toward different groups, which limits the applicability of ML models in many applications, such as crime rate prediction. The data used for ML may have missing values, which, if not appropriately handled, are known to further harmfully affect fairness. To address this issue, many imputation methods have been proposed to deal with missing data. However, research on the effect of missing data imputation on fairness is still rather limited. In this paper, we analyze the impact of imputation on fairness in the context of graph data (node attributes) using different embedding and neural network methods. Extensive experiments on six datasets demonstrate several issues of fairness in graph node classification when dealing with missing data and various imputation techniques. We find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into fairness ML over graph data and how to handle missingness in graphs efficiently.