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.

翻译：我们提出了CounterfactualExplanations.jl：一个用于在Julia中为黑箱模型生成反事实解释（CE）和算法补救（AR）的软件包。CE解释模型输入需要如何改变才能产生特定的模型预测。涉及现实可行且可操作变化的解释可用于提供AR：为个体提供一套建议行动，以将不利结果转变为更好的结果。在本文中，我们讨论了CE在可解释人工智能中的实用性，并展示了该软件包的功能。该软件包使用简便，设计注重定制化和可扩展性。我们希望它有一天能成为通过多样化的反事实生成器解释Julia中任意预测模型的首选工具。