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Analysis of the fairness of machine learning (ML) algorithms recentlyattracted many researchers' interest. Most ML methods show bias towardprotected groups, which limits the applicability of ML models in manyapplications like crime rate prediction etc. Since the data may have missingvalues which, if not appropriately handled, are known to further harmfullyaffect fairness. Many imputation methods are proposed to deal with missingdata. However, the effect of missing data imputation on fairness is not studiedwell. In this paper, we analyze the effect on fairness in the context of graphdata (node attributes) imputation using different embedding and neural networkmethods. Extensive experiments on six datasets demonstrate severe fairnessissues in missing data imputation under graph node classification. We also findthat the choice of the imputation method affects both fairness and accuracy.Our results provide valuable insights into graph data fairness and how tohandle missingness in graphs efficiently. This work also provides directionsregarding theoretical studies on fairness in graph data.

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