Shades of Uncertainty: How AI Uncertainty Visualizations Affect Trust in Alzheimer's Predictions

TL;DR

This study investigates how different visualizations of AI uncertainty influence trust and decision-making in Alzheimer's prognosis, revealing trade-offs and providing guidelines for better clinical AI tools.

Contribution

It introduces a comparative analysis of binary and continuous uncertainty visualizations and offers empirically grounded guidelines for designing trustworthy AI explanations in healthcare.

Findings

Continuous encodings enhance perceived reliability.

Binary encodings boost momentary confidence.

Trade-offs depend on user expertise and uncertainty levels.

Abstract

Artificial intelligence (AI) is increasingly used to support prognosis in Alzheimer's disease (AD), but adoption remains limited due to a lack of transparency and interpretability, particularly for long-term predictions where uncertainty is intrinsic and outcomes may not be known for years. We position uncertainty visualization as an explainable AI (XAI) technique and examine how it shapes trust, confidence, and reliance when users interpret AI-generated forecasts of future cognitive decline transitions. We conducted two studies, one with general participants (N=37) and one with experts in neuroimaging and neurology (N=10), to compare binary (present/absent) and continuous (saturation) uncertainty encodings. Continuous encodings improved perceived reliability and helped users recognize model limitations, while binary encodings increased momentary confidence, revealing expertise-dependent trade-offs in interpreting future predictions under high uncertainty. These findings surface key challenges in designing uncertainty representations for prognostic AI and culminate in a set of empirically grounded guidelines for creating trustworthy, user-appropriate clinical decision support tools.