  Interpretability methods aim to help users build trust in and understand the
capabilities of machine learning models. However, existing approaches often
rely on abstract, complex visualizations that poorly map to the task at hand or
require non-trivial ML expertise to interpret. Here, we present two interface
modules to facilitate a more intuitive assessment of model reliability. To help
users better characterize and reason about a model's uncertainty, we visualize
raw and aggregate information about a given input's nearest neighbors in the
training dataset. Using an interactive editor, users can manipulate this input
in semantically-meaningful ways, determine the effect on the output, and
compare against their prior expectations. We evaluate our interface using an
electrocardiogram beat classification case study. Compared to a baseline
feature importance interface, we find that 9 physicians are better able to
align the model's uncertainty with clinically relevant factors and build
intuition about its capabilities and limitations.
