Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making

TL;DR

This study investigates how decision workflows and explanations influence trust and reliance in human-AI decision making, revealing complex interactions and emphasizing the importance of measuring trust and reliance separately.

Contribution

It provides empirical insights into how different decision workflows and explanations affect trust and reliance, highlighting the nuanced interplay with user knowledge and experience.

Findings

No evidence that 2-step setup reduces overreliance

Workflow did not directly affect self-reported trust

Reported trust and reliance are distinct constructs

Abstract

A central challenge in AI-assisted decision making is achieving warranted, well-calibrated trust. Both overtrust (accepting incorrect AI recommendations) and undertrust (rejecting correct advice) should be prevented. Prior studies differ in the design of the decision workflow - whether users see the AI suggestion immediately (1-step setup) or have to submit a first decision beforehand (2-step setup) -, and in how trust is measured - through self-reports or as behavioral trust, that is, reliance. We examined the effects and interactions of (a) the type of decision workflow, (b) the presence of explanations, and (c) users' domain knowledge and prior AI experience. We compared reported trust, reliance (agreement rate and switch rate), and overreliance. Results showed no evidence that a 2-step setup reduces overreliance. The decision workflow also did not directly affect self-reported trust, but there was a crossover interaction effect with domain knowledge and explanations, suggesting that the effects of explanations alone may not generalize across workflow setups. Finally, our findings confirm that reported trust and reliance behavior are distinct constructs that should be evaluated separately in AI-assisted decision making.