Keywords: Connectomics, graph representation learning, fairness, autism-spectrum disorder
TL;DR: The paper explores fairness of graph structures in a transductive learning setting where a population graph is employed that relies on sensitive attributes.
Abstract: Recent work on neuroimaging has demonstrated significant benefits of using population graphs to capture non-imaging information in the prediction of neurodegenerative and neurodevelopmental disorders. These non-imaging attributes may not only contain demographic information about the individuals, e.g. age or sex, but also the acquisition site, as imaging protocols might significantly differ across sites in large-scale studies. In addition, recent studies have highlighted the need to investigate potential biases in the classifiers devised using large-scale datasets, which might be imbalanced in terms of one or more sensitive attributes. This can be exacerbated when employing these attributes in a population graph to explicitly introduce inductive biases to the machine learning model and lead to disparate predictive performance across sub-populations. In this work, we explore the impact of stratification strategies and graph structures on the fairness of a semi-supervised classifier that relies on a population graph for the prediction of autism-spectrum disorder.
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Paper Type: recently published or submitted journal contributions
Primary Subject Area: Integration of Imaging and Clinical Data
Secondary Subject Area: Application: Other
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