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

• Published in: The AI magazine Vol. 35; no. 4; pp. 105 - 120
• Main Authors: Amershi, Saleema, Cakmak, Maya, Knox, W. Bradley, Kulesza, Todd
• Format: Journal Article
• Language: English
• Published: La Canada American Association for Artificial Intelligence 22.12.2014; John Wiley & Sons, Inc
• Subjects: Algorithms; Artificial intelligence; Behavior; Case studies; Collaboration; Design; End users; Feedback; Human-computer interaction; Interactive computer systems; Interactive learning; Machine learning; Proteins; Recommender systems; Subject specialists; Users; New York
• ISSN: 0738-4602; 2371-9621
• DOI: 10.1609/aimag.v35i4.2513

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.