How Contextual Information in Physical Environments Shapes Decisions with AI

Abstract

We studied whether contextual information helps people make better decisions with fallible AI in a partially visible environment. In an AR study (N = 32), participants judged whether a rendered key was present on a physical desk, with or without AI advice. We varied target prevalence and scene visibility. Focal analyses excluded visible targets and AI suggestions that could be directly checked. Within this subset, the combined context of a stronger prior toward absence (lower prevalence) and a target-free half increased AI-assisted accuracy by 9.5 percentage points. An exploratory analysis accounted for most of this increase through changes in the cases and AI suggestions being evaluated; changed participant responses did not significantly improve accuracy beyond maintaining baseline response patterns. The stronger prior reduced agreement with Present suggestions, while the target-free half lengthened decision time. We did not detect more selective agreement with correct versus incorrect advice. These findings motivate evaluating situated assistance on matched cases and designing ways to obtain evidence that can resolve claims about hidden states.