The notion that algorithmic systems should be "transparent" and "explainable" is common in the many statements of consensus principles developed by governments, companies, and advocacy organizations. But what exactly do policy and legal actors want from these technical concepts, and how do their desiderata compare with the explainability techniques developed in the machine learning literature? In hopes of better connecting the policy and technical communities, we provide case studies illustrating five ways in which algorithmic transparency and explainability have been used in policy settings: specific requirements for explanations; in nonbinding guidelines for internal governance of algorithms; in regulations applicable to highly regulated settings; in guidelines meant to increase the utility of legal liability for algorithms; and broad requirements for model and data transparency. The case studies span a spectrum from precise requirements for specific types of explanations to nonspecific requirements focused on broader notions of transparency, illustrating the diverse needs, constraints, and capacities of various policy actors and contexts. Drawing on these case studies, we discuss promising ways in which transparency and explanation could be used in policy, as well as common factors limiting policymakers' use of algorithmic explainability. We conclude with recommendations for researchers and policymakers.

翻译："算法系统应具有'透明度'和'可解释性'"这一观点，常见于政府、企业和倡导组织所制定的诸多共识原则声明中。但政策与法律行为者究竟希望从这些技术概念中获得什么？他们的需求与机器学习文献中发展的可解释性技术相比又有何异同？为更好连接政策与技术界，我们通过案例研究阐述了算法透明度与可解释性在政策环境中的五种应用方式：针对解释的具体要求；算法内部治理的非约束性指南；适用于强监管场景的法规；旨在增强算法法律责任实用性的指南；以及模型与数据透明度的广泛要求。这些案例覆盖了从特定解释类型的精确要求到侧重广义透明度概念的非具体要求，展现了不同政策行为者与情境的多样化需求、约束及能力。基于这些案例研究，我们探讨了透明度与解释在政策领域的潜在应用方向，以及制约政策制定者运用算法可解释性的常见因素。最后，我们向研究人员与政策制定者提出建议。