Explainability is highly-desired in Machine Learning (ML) systems supporting high-stakes policy decisions in areas such as health, criminal justice, education, and employment. While the field of explainable ML has expanded in recent years, much of this work has not taken real-world needs into account. A majority of proposed methods are designed with \textit{generic} explainability goals without well-defined use-cases or intended end-users and evaluated on simplified tasks, benchmark problems/datasets, or with proxy users (e.g., AMT). We argue that these simplified evaluation settings do not capture the nuances and complexities of real-world applications. As a result, the applicability and effectiveness of this large body of theoretical and methodological work in real-world applications are unclear. In this work, we take steps toward addressing this gap for the domain of public policy. First, we identify the primary use-cases of explainable ML within public policy problems. For each use case, we define the end-users of explanations and the specific goals the explanations have to fulfill. Finally, we map existing work in explainable ML to these use-cases, identify gaps in established capabilities, and propose research directions to fill those gaps to have a practical societal impact through ML. The contribution is 1) a methodology for explainable ML researchers to identify use cases and develop methods targeted at them and 2) using that methodology for the domain of public policy and giving an example for the researchers on developing explainable ML methods that result in real-world impact.

翻译：可解释性是支撑医疗、刑事司法、教育和就业等高风险政策决策的机器学习系统备受推崇的特性。尽管近年来可解释机器学习领域取得了显著发展，但大部分研究并未充分考虑现实世界的实际需求。现有方法大多以“通用性”可解释性为目标，缺乏明确的应用场景或目标终端用户，仅基于简化任务、基准问题/数据集或代理用户（如Amazon Mechanical Turk）进行评估。我们认为，这些简化的评估设置无法反映现实应用中的细微差异与复杂特性，因此大量理论与方法论成果在实际应用中的适用性和有效性仍不明确。本研究旨在弥合公共政策领域这一差距：首先，我们明确了可解释机器学习在公共政策问题中的主要应用场景；其次，针对每个场景定义了可解释性的终端用户及其需达成的具体目标；最后，将现有可解释机器学习研究与这些场景进行映射，识别技术能力的不足，并提出填补空白的研究方向，以期通过机器学习产生切实的社会影响。本文的贡献在于：1）为可解释机器学习研究者提供了一套识别应用场景并开发针对性方法的系统方法论；2）将该方法论应用于公共政策领域，为研究者开发具有现实影响力的可解释机器学习方法提供范例。