Explaining Black-Box Models through Counterfactuals
Patrick Altmeyer Arie van Deursen Cynthia C. S. Liem
We present CounterfactualExplanations.jl: a package for generating
Counterfactual Explanations (CE) and Algorithmic Recourse (AR) for black-box
models in Julia. CE explain how inputs into a model need to change to yield
specific model predictions. Explanations that involve realistic and actionable
changes can be used to provide AR: a set of proposed actions for individuals to
change an undesirable outcome for the better. In this article, we discuss the
usefulness of CE for Explainable Artificial Intelligence and demonstrate the
functionality of our package. The package is straightforward to use and
designed with a focus on customization and extensibility. We envision it to one
day be the go-to place for explaining arbitrary predictive models in Julia
through a diverse suite of counterfactual generators.