Knowing When to Pass: The Effect of AI Reliability in Risky Decision Contexts

Objective: This study manipulates the presence and reliability of AI recommendations for risky decisions to measure the effect on task performance, behavioral consequences of trust, and deviation from a probability matching collaborative decision-making model. Background: Although AI decision support improves performance, people tend to underutilize AI recommendations, particularly when outcomes are uncertain. As AI reliability increases, task performance improves, largely due to higher rates of compliance (following action recommendations) and reliance (following no-action recommendations). Methods: In a between-subject design, participants were assigned to a high reliability AI, low reliability AI, or a control condition. Participants decided whether to bet that their team would win in a series of basketball games tying compensation to performance. We evaluated task performance (in accuracy and signal detection terms) and the behavioral consequences of trust (via compliance and reliance). Results: AI recommendations improved task performance, had limited impact on risk-taking behavior, and were under-valued by participants. Accuracy, sensitivity (d\u27), and reliance increased in the high reliability AI condition, but there was no effect on response bias (c) or compliance. Participant behavior was only consistent with a probability matching model for compliance in the low reliability condition. Conclusion: In a pay-off structure that incentivized risk-taking, the primary value of the AI recommendations was in determining when to perform no action (i.e., pass on bets). Application: In risky contexts, designers need to consider whether action or no-action recommendations will be more influential to design appropriate interventions