The Influence of Exposure and Error Type on Estimates of Automation Reliability

ObjectiveWe evaluated the sensitivity and calibration of users when estimating the reliability of an automated decision support system as exposure to the system and the saliency of errors varied.BackgroundAlthough users are typically sensitive when estimating the reliability of an automated system, it is not known how exposure to a system or error saliency influences the estimate. Viewpoints of the "leaky integrator" and "intuitive statistician" suggest competing outcomes regarding estimates over time.MethodsParticipants identified matching images of MegaBlocks with assistance from an automated system. Participants experienced three levels of reliability and three levels of exposure (number of trials). They provided estimates of reliability, trust, and confidence in their estimates for each system. In Experiment 2, the saliency of false alarms and misses was manipulated. In Experiment 3, the saliency of these error types was matched.ResultsReliability estimates were sensitive to true reliability but were lower with more salient errors. Exposure had no impact on the estimates of reliability or trust. Trust and confidence in the estimate of reliability increased with true reliability.ConclusionsEstimates of reliability are generally sensitive to the true reliability of a system, although they can be hindered by memory limitations and/or saliency effects. Estimates of reliability are made very quickly and do not tend to change with exposure.ApplicationsTraining users on a new system may not require extensive exposure, but early interactions with a system should accurately represent the amount and types of errors that the system will make.