  Analysis of the fairness of machine learning (ML) algorithms recently
attracted many researchers' interest. Most ML methods show bias toward
protected groups, which limits the applicability of ML models in many
applications like crime rate prediction etc. Since the data may have missing
values which, if not appropriately handled, are known to further harmfully
affect fairness. Many imputation methods are proposed to deal with missing
data. However, the effect of missing data imputation on fairness is not studied
well. In this paper, we analyze the effect on fairness in the context of graph
data (node attributes) imputation using different embedding and neural network
methods. Extensive experiments on six datasets demonstrate severe fairness
issues in missing data imputation under graph node classification. We also find
that the choice of the imputation method affects both fairness and accuracy.
Our results provide valuable insights into graph data fairness and how to
handle missingness in graphs efficiently. This work also provides directions
regarding theoretical studies on fairness in graph data.
