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AI recommender systems are sought for decision support by providingsuggestions to operators responsible for making final decisions. However, thesesystems are typically considered black boxes, and are often presented withoutany context or insight into the underlying algorithm. As a result, recommendersystems can lead to miscalibrated user reliance and decreased situationawareness. Recent work has focused on improving the transparency of recommendersystems in various ways such as improving the recommender's analysis andvisualization of the figures of merit, providing explanations for therecommender's decision, as well as improving user training or calibrating usertrust. In this paper, we introduce an alternative transparency technique ofstructuring the order in which contextual information and the recommender'sdecision are shown to the human operator. This technique is designed to improvethe operator's situation awareness and therefore the shared situation awarenessbetween the operator and the recommender system. This paper presents theresults of a two-phase between-subjects study in which participants and arecommender system jointly make a high-stakes decision. We varied the amount ofcontextual information the participant had, the assessment technique of thefigures of merit, and the reliability of the recommender system. We found thatproviding contextual information upfront improves the team's shared situationawareness by improving the human decision maker's initial and final judgment,as well as their ability to discern the recommender's error boundary.Additionally, this technique accurately calibrated the human operator's trustin the recommender. This work proposes and validates a way to providemodel-agnostic transparency into AI systems that can support the human decisionmaker and lead to improved team performance.

Improving Operator Situation Awareness when Working with AI Recommender Systems