AAAI Publications, Twenty-Fifth AAAI Conference on Artificial Intelligence

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Effective End-User Interaction with Machine Learning
Saleema Amershi, James Fogarty, Ashish Kapoor, Desney Tan

Last modified: 2011-08-04

Abstract


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.

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