The (Im)perfect Automation Schema: Who Is Trusted More, Automated or Human Decision Support?

Objective: This study's purpose was to better understand the dynamics of trust attitude and behavior in human-agent interaction. Background: Whereas past research provided evidence for a perfect automation schema, more recent research has provided contradictory evidence. Method: To disentangle these conflicting findings, we conducted an online experiment using a simulated medical Xray task. We manipulated the framing of support agents (i.e., artificial intelligence (AI) versus expert versus novice) between-subjects and failure experience (i.e., perfect support, imperfect support, back-to-perfect support) within subjects. Trust attitude and behavior as well as perceived reliability served as dependent variables. Results: Trust attitude and perceived reliability were higher for the human expert than for the AI than for the human novice. Moreover, the results showed the typical pattern of trust formation, dissolution, and restoration for trust attitude and behavior as well as perceived reliability. Forgiveness after failure experience did not differ between agents. Conclusion: The results strongly imply the existence of an imperfect automation schema. This illustrates the need to consider agent expertise for human-agent interaction. Application: When replacing human experts with AI as support agents, the challenge of lower trust attitude towards the novel agent might arise.