Explainable AI: A Review of the Empirical Literature

Abstract

AI deep learning models have become more capable, but also more complex and less explainable. To address this development, new research on so-called explainable AI (XAI) has proliferated. This paper surveys the empirical literature on XAI based on human-subject experiments. It classifies extant work across different technical and experimental dimensions. Our findings suggest that explainable AI improves self-reported understanding and trust in AI. However, this rarely translates into improved performance of humans in incentivized tasks with AI support. We list several implications of these findings concerning the use of explainable AI in human-computer interaction.

Keywords: Explainable AI, human perception, experiments, human-computer interaction