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Toward Harnessing User Feedback For Machine Learning

By Simone Stumpf, Vidya Rajaram, Lida Li, Margaret Burnett, Thomas Dietterich, Erin Sullivan, Russell Drummond and Jonathan Herlocker

Abstract

There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource—the users themselves—could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users ’ understanding and trust of the system could improve as well. We conducted a think-aloud study to see how willing users were to provide feedback and to understand what kinds of feedback users could give. Users were shown explanations of machine learning predictions and asked to provide feedback to improve the predictions. We found that users had no difficulty providing generous amounts of feedback. The kinds of feedback ranged from suggestions for reweighting of features to proposals for new features, feature combinations, relational features, and wholesale changes to the learning algorithm. The results show that user feedback has the potential to significantly improve machine learning systems, but that learning algorithms need to be extended in several ways to be able to assimilate this feedback. ACM Classification: H.5.2 [Information interfaces and presentation (e.g., HCI)] User Interfaces: Theory and methods, Evaluation/methodology. H.1.2 [Models and Principles]: User/Machine Systems: Human information processing

Topics: Machine learning, explanations, user feedback for
Year: 2008
OAI identifier: oai:CiteSeerX.psu:10.1.1.129.2393
Provided by: CiteSeerX
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