Effective End-User Interaction with Machine Learning

End-user interactive machine learning is a promising tool for enhancing human productivity and capabilities with large unstructured data sets. Recent work has shown that we can create end-user interactive machine learning systems for specific applications. However, we still lack a generalized understanding of how to design effective end-user interaction with interactive machine learning systems. This work presents three explorations in designing for effective end-user interaction with machine learning in CueFlik, a system developed to support Web image search. These explorations demonstrate that interactions designed to balance the needs of end-users and machine learning algorithms can significantly improve the effectiveness of end-user interactive machine learning.

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: Saleema Amershi, James Fogarty, Ashish Kapoor, Desney S. Tan
• Keywords: AAAI 2011, Human Productivity, Intelligent Agents, Interactive Machine, Machine Learning Algorithms