  Machine learning (ML) models that achieve high average accuracy can still
underperform on semantically coherent subsets ("slices") of data. This behavior
can have significant societal consequences for the safety or bias of the model
in deployment, but identifying these underperforming slices can be difficult in
practice, especially in domains where practitioners lack access to group
annotations to define coherent subsets of their data. Motivated by these
challenges, ML researchers have developed new slice discovery algorithms that
aim to group together coherent and high-error subsets of data. However, there
has been little evaluation focused on whether these tools help humans form
correct hypotheses about where (for which groups) their model underperforms. We
conduct a controlled user study (N = 15) where we show 40 slices output by two
state-of-the-art slice discovery algorithms to users, and ask them to form
hypotheses about an object detection model. Our results provide positive
evidence that these tools provide some benefit over a naive baseline, and also
shed light on challenges faced by users during the hypothesis formation step.
We conclude by discussing design opportunities for ML and HCI researchers. Our
findings point to the importance of centering users when creating and
evaluating new tools for slice discovery.
