User's confidence in machine learning (ML) based decision making significantly affects acceptability of ML techniques. In this work, we investigate how uncertainty/correlation affects user's confidence in order to design effective user interface for ML-based intelligent systems. A user study was performed and we found that revealing of correlation helped users better understand uncertainty and thus increased confidence in model output. When correlation had the same trend with performance, correlation but not uncertainty helped users more confident in their decisions.
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