Download PDFOpen PDF in browserAn Empirical Study on Automation Transparency (i.e., seeing-into) of an Automated Decision Aid System for Condition-based MaintenanceEasyChair Preprint 56568 pages•Date: May 28, 2021AbstractPrior studies have shown conflicting results about the impact of information disclosure on human performance– often referred to as transparency (i.e., seeing-into) studies. We conducted an experiment to investigate whether transparency manipulations predicted whether participants could identify whether features and their relative weights of a decision aid guided by a Machine Learning model were consistent with stated best practices for making maintenance decisions. We had insignificant results on state estimation, automation reliance, trust, workload, and self-confidence. This study shows that disclosing information about the decision aid rationale does not necessarily impact operator performance. Keyphrases: Automation Transparency, Decision aids, Maintenance, human performance
|