Understanding the Effects of Miscalibrated AI Confidence on User Trust, Reliance, and Decision Efficacy
Jingshu Li; Yitian Yang; Renwen Zhang; Q. Vera Liao; Tianqi Song; Zhengtao Xu; Yi-chieh Lee · 2024 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Providing well-calibrated AI confidence can help promote users' appropriate trust in and reliance on AI, which are essential for AI-assisted decision-making. However, calibrating AI confidence -- providing confidence score that accurately reflects the true likelihood of AI being correct -- is known to be challenging. To understand the effects of AI confidence miscalibration, we conducted our first experiment. The results indicate that miscalibrated AI confidence impairs users' appropriate reliance and reduces AI-assisted decision-making efficacy, and AI miscalibration is difficult for users to
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Leakage and the reproducibility crisis in machine-learning-based science
Negative / Null Result ReportDefining and detecting quantum speedup
Negative / Null Result ReportService robots in hotels: understanding the service quality perceptions of human-robot interaction
Negative / Null Result ReportBoosting methods for multi-class imbalanced data classification: an experimental review
Negative / Null Result ReportFINANCIAL DEVELOPMENT AND ECONOMIC GROWTH: A META‐ANALYSIS
Negative / Null Result ReportThe impact of site-specific digital histology signatures on deep learning model accuracy and bias
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: arXiv