In Machine Learning (ML) models used for supporting decisions in high-stakes domains such as public policy, explainability is crucial for adoption and effectiveness. While the field of explainable ML has expanded in recent years, much of this work does not take real-world needs into account. A majority of proposed methods use benchmark ML problems with generic explainability goals without clear use-cases or intended end-users. As a result, the effectiveness of this large body of theoretical and methodological work on real-world applications is unclear. This paper focuses on filling this void for the domain of public policy. We develop a taxonomy of explainability use-cases within public policy problems; for each use-case, we define the end-users of explanations and the specific goals explainability has to fulfill; third, we map existing work to these use-cases, identify gaps, and propose research directions to fill those gaps in order to have practical policy impact through ML.

本文对机器学习中的可解释性问题进行研究，通过研究公共政策问题中的主要应用场景，确定解释的最终用户和解释需要达到的特定目标，并针对这些用例，映射解释性机器学习的现有工作，确定已有能力的差距，并提出填补这些差距的研究方向，以在实际社会中产生影响。

公共政策可解释机器学习:应用案例、差距和研究方向