Power to the People: The Role of Humans in Interactive Machine Learning
- 1 December 2014
- journal article
- Published by Wiley in AI Magazine
- Vol. 35 (4), 105-120
- https://doi.org/10.1609/aimag.v35i4.2513
Abstract
Intelligent systems that 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 characterize the impact of interactivity, demonstrate ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. We argue that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives. After giving a glimpse of the progress that has been made so far, we discuss the challenges that we face in moving the field forward.Keywords
This publication has 31 references indexed in Scilit:
- Why-oriented end-user debugging of naive Bayes text classificationACM Transactions on Interactive Intelligent Systems, 2011
- Towards Understanding How Humans Teach RobotsLecture Notes in Computer Science, 2011
- Adaptive Interfaces and AgentsPublished by Taylor & Francis Ltd ,2009
- Teachable robots: Understanding human teaching behavior to build more effective robot learnersArtificial Intelligence, 2008
- Cobot in LambdaMOO: An Adaptive Social Statistics AgentAutonomous Agents and Multi-Agent Systems, 2006
- Interactive machine learningPublished by Association for Computing Machinery (ACM) ,2003
- Steps to take before intelligent user interfaces become realInteracting with Computers, 2000
- Principles of mixed-initiative user interfacesPublished by Association for Computing Machinery (ACM) ,1999
- A desicion-theoretic generalization of on-line learning and an application to boostingLecture Notes in Computer Science, 1995
- How might people interact with agentsCommunications of the ACM, 1994