The Choice to Use Automation: Improvements from Evidence Accumulation

Abstract

This study examined discretionary automation–the choice to engage optional, automated support. In a dynamic decision-making task, after manual and aided trials, participants chose whether to engage a machine learning decision aid. The aid improved performance but participants frequently disused automation and lagged in compliance. Experiment 2 forced greater evidence accumulation, improving compliance, automation use, and performance. Experiment 3 required an initial judgment coupled with subsequent evidence accumulation. This reduced automation use but retained higher levels of compliance and performance. Across experiments, trust in automation and self-confidence influenced use decisions, but task difficulty had little impact. Implications for human-automation systems are discussed.