Missing Data Imputation and the Fairness-Accuracy Trade-off in Graph Neural Networks Versus Standard Node Classifiers

Abstract

Abstract: 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 dat

Research Question

How does missing data imputation affect the fairness-accuracy trade-off in graph neural networks compared to standard node classifiers?

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