Paper Type
ERF
Abstract
This study advances trust calibration research by examining AI-supported infectious disease triage, where uncertainty and constrained feedback limit users’ ability to evaluate system performance. By focusing on time-sensitive reliance decisions under information asymmetry, it extends calibration research beyond low-stakes, feedback-rich settings. We employ a quasi-field experiment in which participants make accept-or-reject decisions on AI triage recommendations embedded in clinically realistic scenarios, allowing direct observation of reliance behavior across varying levels of system correctness and feedback clarity.
Paper Number
1184
Recommended Citation
Yang, Ning and Malliaris, Mary, "Trust Calibration and Patient Decision in AI-Supported Infectious Disease Triage" (2026). AMCIS 2026 Proceedings. 1.
https://aisel.aisnet.org/amcis2026/sig_hci/sig_hci/1
Trust Calibration and Patient Decision in AI-Supported Infectious Disease Triage
This study advances trust calibration research by examining AI-supported infectious disease triage, where uncertainty and constrained feedback limit users’ ability to evaluate system performance. By focusing on time-sensitive reliance decisions under information asymmetry, it extends calibration research beyond low-stakes, feedback-rich settings. We employ a quasi-field experiment in which participants make accept-or-reject decisions on AI triage recommendations embedded in clinically realistic scenarios, allowing direct observation of reliance behavior across varying levels of system correctness and feedback clarity.
Comments
SIG HCI