The Effect of Type of Explanation on Algorithm Appreciation: The Role of Risk Perceptions in Healthcare Decision-Making

Abstract

This study examines the impact of various types of Artificial Intelligence (AI) explanations—local, counterfactual, and global—on individuals' algorithm appreciation in healthcare decision-making. Using a scenario-based experiment involving 611 participants, we take a risk perspective to examine how eXplainable (XAI) system credibility (risk probability) and perceived condition severity (risk severity) mediate the relationship between explanation type and algorithm appreciation. We also explore how decision-makers’ risk-taking propensity (risk perception) moderates these relationships. Participants assessed diabetes risk predictions for a hypothetical relative based on explanations generated by an XAI system. Findings reveal that explanation type significantly influences algorithm appreciation through the perceived severity of the condition, but not through the credibility of the XAI system. Importantly, the effects of explanation type vary with participants' risk-taking propensity. Hence, this research highlights the need for personalized XAI strategies to maximize algorithm appreciation in high-risk healthcare decision-making contexts involving non-expert decision-makers.

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