Experience Over Explanation: Perceived Transparency in AI-Based Skin Cancer Detection

Abstract

Artificial intelligence (AI) is increasingly integrated into everyday life and holds great potential for high-stakes domains such as healthcare, for example in early skin cancer detection. However, user trust remains a major barrier to adoption, prior research has largely treated explainable AI (XAI) approaches as universally applicable rather than accounting for individual differences. In this study, we investigate how three XAI formats (mechanism–modality pairings) shape user trust through perceived transparency in AI-powered skin cancer diagnostics. Using a between-subjects online experiment with 15 dermoscopic images, we show that the effect of XAI format on trust is fully mediated by perceived transparency, and this is significantly moderated by users’ AI experience. Notably, AI experience can reverse the effect, underscoring the importance of tailoring explanations to user backgrounds. These findings advance the understanding of how trust in AI can be more appropriately calibrated and provide guidance for designing personalized XAI in healthcare.

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