Expertise Levels of Human versus Automated Decision Aids Influence Response Biases in a Visual Search Task

Abstract

Studies have demonstrated that humans appear to apply norms of human-human interaction to interaction with automated decision aids. We examined the differences in perceptions of automation vs. humans when the expertise and reliability of these advisers varied. Participants (n = 180) performed a luggage-screening task with the assistance of human or automated advisers that differed in pedigree (expert vs. novice) and reliability (high vs. low), but had a similar neutral beta setting of 1.0. Shifts in sensitivity, criterion settings and accuracy were assessed. Participants who were presented with a low-reliable “expert” adviser shifted their bias away from the neutral bias of the adviser and more toward optimal beta compared to participants receiving unreliable advice from a ‘novice’. This effect increased across trials for participants using low-reliability automated advisers but not human advisers. The results have implications for the development of models of optimal utilization of decision aids.

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