Calibrating Trust Towards An Autonomous Image Classifier, 2019

Successful adoption of autonomous systems requires appropriate trust from human users, with trust calibrated to reflect true system performance. Autonomous image classifiers are one such example and can be used in a variety of settings to independently identify the contents of image data. We investigated users’ trust when collaborating with an autonomous image classifier system that we created using the AlexNet model (Krizhevsky et al., 2012). Participants collaborated with the classifier during an image classification task in which the classifier provided labels that either correctly or incorrectly described the contents of images. This task was complicated by the quality of the images processed by the human-classifier team: 50% of the trials featured images that were cropped and blurred, thereby partially obscuring their contents. Across 160 single-image trials, we examined trust towards the classifier, while we also looked at how participants complied with the classifier by accepting or rejecting the labels it provided. Furthermore, we investigated whether trust towards the classifier could be improved by increasing the transparency of the classifier’s interface, by displaying system confidence information in three different ways, which were compared to a control interface without confidence information. Results showed that trust towards the classifier was primarily based on system performance, yet this also was influenced by the quality of the images and individual differences amongst participants. While participants typically preferred classifier interfaces that presented confidence information, it did not appear to improve participants’ trust towards the classifier.