The Problem of Explanations without User Feedback
نویسندگان
چکیده
Explanations are necessary for building users’ understanding and trust in machine learning systems. However, users may abandon systems if these explanations demonstrate consistent errors and they cannot affect change in the systems’ behavior in response. When user feedback is supported, then the utility of explanations is to not only promote understanding, but also enable users to help the machine learning system overcome errors. We suggest an experiment to examine how users react when a system makes explainable mistakes with varied support for user feedback. Author
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