Fairness-related performance and explainability effects in deep learning models for brain image analysis.

ABSTRACT: Purpose: Explainability and fairness are two key factors for the effective and ethical clinical implementation of deep learning-based machine learning models in healthcare settings. However, there has been limited work on investigating how unfair performance manifests in explainable artificial intelligence (XAI) methods, and how XAI can be used to investigate potential reasons for unfairness. Thus, the aim of this work was to analyze the effects of previously established sociodemographic-related confounders on classifier performance and explainability methods. Approach: A convolutional neural network (CNN) was trained to predict biological sex from T1-weighted brain MRI datasets of 4547 9- to 10-year-old adolescents from the Adolescent Brain Cognitive Development study. Perfo

SUBMITTER: Stanley EAM 

PROVIDER: S-EPMC9412191 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature