  The continued improvements in the predictive accuracy of machine learning
models have allowed for their widespread practical application. Yet, many
decisions made with seemingly accurate models still require verification by
domain experts. In addition, end-users of a model also want to understand the
reasons behind specific decisions. Thus, the need for interpretability is
increasingly paramount. In this paper we present an interactive visual
analytics tool, ViCE, that generates counterfactual explanations to
contextualize and evaluate model decisions. Each sample is assessed to identify
the minimal set of changes needed to flip the model's output. These
explanations aim to provide end-users with personalized actionable insights
with which to understand, and possibly contest or improve, automated decisions.
The results are effectively displayed in a visual interface where
counterfactual explanations are highlighted and interactive methods are
provided for users to explore the data and model. The functionality of the tool
is demonstrated by its application to a home equity line of credit dataset.
