Research and Analysis of Trust and Control in Human-AI Interaction for Decision-Making Systems in Optimizing Public Health Service

Abstract

This research investigates the interplay between trust and control in the context of AI-enabled healthcare decision-making using a cybernetic approach to how clinicians adjust their trust toward AI-powered clinical decision support systems. It seeks to uncover the barriers to AI implementation within clinical practices regarding dependency and trust towards AI technologies. Forty-two clinicians who used an AI-powered clinical decision support system in radiology participated in an empirical mixed-method study. Participants interpreted x-ray images with different levels of AI control and transparency. Trust ratings, override behaviors, and decision accuracy were captured and analyzed with t-tests, ANOVA, and regression models. Trust dynamics were explored through semi-structured interviews. The findings reveal that when the AI adheres to clinical reasoning, its transparency boosts trust, yet it diminishes confidence when an error occurs.

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