Not All Information Is Equal: Effects of Disclosing Different Types of Likelihood Information on Trust,Compliance and Reliance,and Task Performance in Human-Automation Teaming

Abstract

Objective

The study examines the effects of disclosing different types of likelihood information on human operators’ trust in automation, their compliance and reliance behaviors, and the human-automation team performance.

Background

To facilitate appropriate trust in and dependence on automation, explicitly conveying the likelihood of automation success has been proposed as one solution. Empirical studies have been conducted to investigate the potential benefits of disclosing likelihood information in the form of automation reliability, (un)certainty, and confidence. Yet, results from these studies are rather mixed.

Method

We conducted a human-in-the-loop experiment with 60 participants using a simulated surveillance task. Each participant performed a compensatory tracking task and a threat detection task with the help of an imperfect automated threat detector. Three types of likelihood information were presented: overall likelihood information, predictive values, and hit and correct rejection rates. Participants’ trust in automation, compliance and reliance behaviors, and task performance were measured.

Results

Human operators informed of the predictive values or the overall likelihood value, rather than the hit and correct rejection rates, relied on the decision aid more appropriately and obtained higher task scores.

Conclusion

Not all likelihood information is equal in aiding human-automation team performance. Directly presenting the hit and correct rejection rates of an automated decision aid should be avoided.

Application

The findings can be applied to the design of automated decision aids.

Keywords

human–robot interaction trust in automation likelihood alerts Bayesian inference base rate fallacy

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