An Empirical Study on Automation Transparency (i.e., seeing-into) of an Automated Decision Aid System for Condition-Based Maintenance

Fahimeh Rajabiyazdi,Greg A. Jamieson, David Quispe Guanolusia

Proceedings of the 21st Congress of the International Ergonomics Association (IEA 2021)Lecture Notes in Networks and Systems(2021)

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Prior 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.
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Key words
automation transparency,automated decision aid system,maintenance,seeing-into,condition-based
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