Not All Trust is the Same: Effects of Decision Workflow and Explanations in Human-AI Decision Making
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...
| Authors | Laura Spillner, Rachel Ringe, Robert Porzel et al. |
|---|---|
| Primary category | cs.HC |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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