Understanding Trust and Reliance Development in AI Advice: Assessing Model Accuracy, Model Explanations, and Experiences from Previous Interactions

Abstract

People are increasingly interacting with AI systems, but successful interactions depend on people trusting these systems only when appropriate. Since neither gaining trust in AI advice nor restoring lost trust after AI mistakes is warranted, we seek to better understand the development of trust and reliance in sequential human-AI interaction scenarios. In a 2 \({\times}\) 2 between-subject simulated AI experiment, we tested how model accuracy (high vs. low) and explanation type (human-like vs. abstract) affect trust and reliance on AI advice for repeated interactions. In the experiment, participants estimated jail times for 20 criminal law cases, first without and then with AI advice. Our results show that trust and reliance are significantly higher for high model accuracy. In addition, reliance does not decline over the trial sequence, and trust increases significantly with high accuracy. Human-like (vs. abstract) explanations only increased reliance on the high-accuracy condition. We furthermore tested the extent to which trust and reliance in a trial round can be explained by trust and reliance experiences from prior rounds. We find that trust assessments in prior trials correlate with trust in subsequent ones. We also find that the cumulative trust experience of a person in all earlier trial rounds correlates with trust in subsequent ones. Furthermore, we find that the two trust measures, trust and reliance, impact each other: prior trust beliefs not only influence subsequent trust beliefs but likewise influence subsequent reliance behavior, and vice versa. Executing a replication study yielded comparable results to our original study, thereby enhancing the validity of our findings.

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