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The Influence of Exposure and Error Type on Estimates of Automation Reliability
Colleen E Patton1, Amelia C Warden2, Ebernoe Guzman-Bonilla1
1North Carolina State University, USA.
Human Factors
|March 19, 2026
Summary
User estimates of automated system reliability are sensitive to true reliability but can be lowered by salient errors. Increased exposure to the system did not change reliability or trust estimates.
Area of Science:
- Human-Computer Interaction
- Cognitive Psychology
- Artificial Intelligence
Background:
- Users typically estimate automated system reliability accurately.
- The influence of system exposure and error saliency on reliability estimation is not well understood.
- Theoretical viewpoints suggest competing effects of exposure on user estimates over time.
Purpose of the Study:
- To evaluate user sensitivity and calibration in estimating automated decision support system reliability.
- To investigate how system exposure and error saliency affect these estimations.
Main Methods:
- Participants estimated system reliability, trust, and confidence while using an automated image-matching system.
- Experiment manipulated system reliability, user exposure (number of trials), and error saliency (false alarms, misses).
Main Results:
- Reliability estimates correlated with true system reliability but decreased with more salient errors.
- System exposure did not significantly impact reliability or trust estimates.
- Trust and confidence in reliability estimates increased with higher true reliability.
Conclusions:
- User reliability estimates are generally sensitive to actual system performance but can be affected by memory and error saliency.
- Estimates are formed quickly and are not significantly altered by prolonged exposure.
- Training should focus on accurate initial error representation rather than extensive exposure.
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