Leveraging Explanations in Interactive Machine Learning: An Overview

The fields of eXplainable Artificial Intelligence (XAI) and Interactive Machine Learning (IML) have traditionally been explored separately. On the one hand, XAI aims at making AI and Machine Learning (ML) systems more transparent and understandable, chiefly by equipping them with algorithms for explaining their own decisions [66, 125]. Such explanations are instrumental for enabling stakeholders to inspect the system's knowledge and reasoning patterns, however stakeholders only participate as passive observers and have no control over the system or its behavior. On the other hand, IML focuses primarily on communication between machines and humans, and it is specifically concerned with eliciting and incorporating human feedback into the training process via intelligent user interfaces [53, 10, 109, 176, 71, 173]. IML covers a broad range of techniques for in-the-loop interaction between humans and machines, however, most research does not explicitly consider explanations. Recently, a number of works have sought integrating techniques from XAI within the IML loop. The core observation behind this line of research is that, interacting through explanations is an elegant and human-centric solution to the problem of acquiring rich human feedback, and therefore leads to higher-quality AI and ML systems, in a manner that is effective and transparent for both users and machines. In order to accomplish this vision, these works leverage either machine explanations obtained using techniques from XAI, human explanations provided as feedback by sufficiently expert annotators, or both, to define and implement a suitable interaction protocol.