  This paper investigates the user experience of visualizations of a machine
learning (ML) system that recognizes objects in images. This is important since
even good systems can fail in unexpected ways as misclassifications on
photo-sharing websites showed. In our study, we exposed users with a background
in ML to three visualizations of three systems with different levels of
accuracy. In interviews, we explored how the visualization helped users assess
the accuracy of systems in use and how the visualization and the accuracy of
the system affected trust and reliance. We found that participants do not only
focus on accuracy when assessing ML systems. They also take the perceived
plausibility and severity of misclassification into account and prefer seeing
the probability of predictions. Semantically plausible errors are judged as
less severe than errors that are implausible, which means that system accuracy
could be communicated through the types of errors.
