Impact of Missing Data Imputation Techniques on Demographic Parity and Equalized Odds in Graph-Based Node Classification

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 goal: What is the impact of different missing data imputation techniques on the demographic parity and equalized odds of graph-based node classification models?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.5/10.