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With machine learning models being increasingly applied to variousdecision-making scenarios, people have spent growing efforts to make machinelearning models more transparent and explainable. Among various explanationtechniques, counterfactual explanations have the advantages of beinghuman-friendly and actionable -- a counterfactual explanation tells the userhow 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 counterfactualexplanations to understand and explore the behavior of machine learning models.We design DECE, an interactive visualization system that helps understand andexplore a model's decisions on individual instances and data subsets,supporting users ranging from decision-subjects to model developers. DECEsupports exploratory analysis of model decisions by combining the strengths ofcounterfactual explanations at instance- and subgroup-levels. We also introducea set of interactions that enable users to customize the generation ofcounterfactual explanations to find more actionable ones that can suit theirneeds. Through three use cases and an expert interview, we demonstrate theeffectiveness of DECE in supporting decision exploration tasks and instanceexplanations.

DECE: Decision Explorer with Counterfactual Explanations for Machine Learning Models