Arguments in Interactive Machine Learning

Abstract

In most applications of machine learning, domain experts provide domain specic knowledge. From previous experience it is known that domain experts are unable to provide all relevant knowledge in advance, but need to see some results of machine learning rst. Interactive machine learning, where experts and machine learning algorithm improve the model in turns, seems to solve this problem. In this position paper, we propose to use arguments in interaction between machine learning and experts. Since using and understanding arguments is a practical skill that humans learn in everyday life, we believe that arguments will help experts to better understand the models, facilitate easier elicitation of new knowledge from experts, and can be intuitively integrated in machine learning. We describe an argument-based dialogue, which is based on a series of steps

such as questions and arguments, that can help obtain from a domain expert exactly

that knowledge which is missing in the current model.