Power to the People: The Role of Humans in Interactive Machine Learning

TLDR

Interactive machine‑learning systems are rapidly proliferating, yet their development has largely focused on algorithmic advances while researchers increasingly recognize the need to study users. This article advocates a user‑centric approach to interactive machine learning, illustrating how it can improve user experience and system effectiveness while outlining future challenges. Through case studies, the authors demonstrate how interactivity creates a tight system–user coupling, highlight shortcomings of current designs, and propose novel interaction strategies.

Abstract

Systems that can learn interactively from their end‐users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that demonstrate how interactivity results in a tight coupling between the system and the user, exemplify ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. After giving a glimpse of the progress that has been made thus far, we discuss some of the challenges we face in moving the field forward.