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Why-oriented end-user debugging of naive Bayes text classification

TitleWhy-oriented end-user debugging of naive Bayes text classification
Publication TypeJournal Article
Year of Publication2011
AuthorsKulesza, T., S. Stumpf, W-K. Wong, M. M. Burnett, S. Perona, A. J. Ko, and I. Oberst
JournalACM Transactions on Interactive Intelligent Systems
Pagination1 - 31
Date Published10/2011
Keywordsdebugging, design, end-user programming, experimentation, human factors, machine learning, performance, user interfaces, user/machine systems

Machine learning techniques are increasingly used in intelligent assistants, that is, software targeted at and continuously adapting to assist end users with email, shopping, and other tasks. Examples include desktop SPAM filters, recommender systems, and handwriting recognition. Fixing such intelligent assistants when they learn incorrect behavior, however, has received only limited attention. To directly support end-user “debugging” of assistant behaviors learned via statistical machine learning, we present a Why-oriented approach which allows users to ask questions about how the assistant made its predictions, provides answers to these “why” questions, and allows users to interactively change these answers to debug the assistant's current and future predictions. To understand the strengths and weaknesses of this approach, we then conducted an exploratory study to investigate barriers that participants could encounter when debugging an intelligent assistant using our approach, and the information those participants requested to overcome these barriers. To help ensure the inclusiveness of our approach, we also explored how gender differences played a role in understanding barriers and information needs. We then used these results to consider opportunities for Why-oriented approaches to address user barriers and information needs.

Short TitleACM Trans. Interact. Intell. Syst.TiiS