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Appropriate reliance is critical to achieving synergistic human-AIcollaboration. For instance, when users over-rely on AI assistance, theirhuman-AI team performance is bounded by the model's capability. This workstudies how the presentation of model uncertainty may steer users'decision-making toward fostering appropriate reliance. Our results demonstratethat showing the calibrated model uncertainty alone is inadequate. Rather,calibrating model uncertainty and presenting it in a frequency format allowusers to adjust their reliance accordingly and help reduce the effect ofconfirmation bias on their decisions. Furthermore, the critical nature of ourskin cancer screening task skews participants' judgment, causing their relianceto vary depending on their initial decision. Additionally, step-wise multipleregression analyses revealed how user demographics such as age and familiaritywith probability and statistics influence human-AI collaborativedecision-making. We discuss the potential for model uncertainty presentation,initial user decision, and user demographics to be incorporated in designingpersonalized AI aids for appropriate reliance.

Designing for Appropriate Reliance: The Roles of AI Uncertainty  Presentation, Initial User Decision, and User Demographics in AI-Assisted  Decision-Making