Before revision:  In this paper , we propose a novel paradigm of explainable active learning (XAL), by explaining the learning algorithm's prediction for the instance it wants to learn from and soliciting feedback from the annotator. We conduct an empirical study comparing the model learning outcome, human feedback content and the annotator experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation).
After revision:  In this paper , we propose a novel paradigm of explainable active learning (XAL), by introducing techniques from the recently surging field of explainable AI (XAI) into an AL setting. We conducted an empirical study comparing the model learning outcome, human feedback content and the annotator experience with XAL, to that of traditional AL and coactive learning (providing the model's prediction without the explanation).