Advancing Fairness and Explainability in AI for Autism Diagnosis

Abstract

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition that is often underdiagnosed, and AI presents a promising approach for scalable, early detection using behavioral and neuroimaging data. Despite advances in this area, the lack of comprehensive datasets, along with insufficient attention to fairness and interpretability—two critical factors for the clinical adoption of AI—remains a significant challenge. This study enhances an existing AI-based ASD diagnostic pipeline by applying multiple imputation techniques (mean, median, and KNN) to address missing data in a comprehensive dataset combining behavioral and neuroimaging features, while incorporating gender fairness evaluation with bias mitigation strategies and enhancing model explainability. Results indicate that KNN imputation yields superior model performance, while bias mitigation using the Threshold Optimizer significantly reduces gender disparities without compromising accuracy. Additionally, SHAP visualizations provide interpretable predictions at both global and individual levels. Our findings demonstrate that careful attention to these components can yield more equitable and transparent ML systems, paving the way for responsible AI use in clinical settings.