Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making
This research addresses the challenge of achieving warranted trust in AI-assisted decision-making for system designers and researchers, particularly concerning the impact of workflow design and explanations.
This paper investigated how decision workflow (1-step vs. 2-step) and the presence of explanations affect human trust and reliance on AI in decision-making. The study found no evidence that a 2-step setup reduces overreliance, and the decision workflow did not directly impact self-reported trust.
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.