  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 visual
analytics modules that facilitate an 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. 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 14 physicians are better able to
align the model's uncertainty with domain-relevant factors and build intuition
about its capabilities and limitations.
