Exploring the Effects of Machine Learning Algorithms of Varying Transparency on Performance Outcomes

Abstract

Machine learning algorithms are becoming increasingly used in a variety of settings but are often black box in nature. Recent work has emphasized the need for algorithms to be more interpretable to end users, and calibrated classification models (CCMs) are one such type of model. CCMs provide more accurate confidence intervals to the end user, however little research has investigated how CCM confidence estimates and actual classification accuracy impact user performance. Therefore, the current study explored how expectations for machine learning algorithms and their actual behaviors influenced task performance and decision time. Results demonstrated that algorithms with high confidence and low classification accuracy led to the lowest performance and highest decision time in an image classification task. Limitations of the current study are discussed along with future research opportunities.