  With machine learning models being increasingly applied to various
decision-making scenarios, people have spent growing efforts to make machine
learning models more transparent and explainable. Among various explanation
techniques, counterfactual explanations have the advantages of being
human-friendly and actionable -- a counterfactual explanation tells the user
how to gain the desired prediction with minimal changes to the input. Besides,
counterfactual explanations can also serve as efficient probes to the models'
decisions. In this work, we exploit the potential of counterfactual
explanations to understand and explore the behavior of machine learning models.
We design DECE, an interactive visualization system that helps understand and
explore a model's decisions on individual instances and data subsets,
supporting users ranging from decision-subjects to model developers. DECE
supports exploratory analysis of model decisions by combining the strengths of
counterfactual explanations at instance- and subgroup-levels. We also introduce
a set of interactions that enable users to customize the generation of
counterfactual explanations to find more actionable ones that can suit their
needs. Through three use cases and an expert interview, we demonstrate the
effectiveness of DECE in supporting decision exploration tasks and instance
explanations.
