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.
Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty
Partnership on AI（人工智能合作组织）；University of Cambridge（剑桥大学）；IBM Research（IBM研究院）；Carnegie Mellon University（卡内基梅隆大学）；Element AI；Intel Labs（英特尔实验室）；New York University（纽约大学）；The Alan Turing Institute（艾伦·图灵研究所）；Sony AI（索尼AI）
Generative Example-Based Explanations: Bridging the Gap between Generative Modeling and Explainability
Center for Artificial Intelligence and Robotics（人工智能与机器人中心）；Technical University of Applied Sciences Würzburg-Schweinfurt（应用科学大学魏玛-施维林分校）；Bielefeld University（比勒菲尔德大学）
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements
Institute of Software Engineering University of Stuttgart Stuttgart, Germany Email；Institute of Software Engineering University of Stuttgart Stuttgart, Germany
Transparency and Proportionality in Post-Processing Algorithmic Bias Correction
Data Science and Computational Intelligence Institute (DaSCI), University of Granada（数据科学与计算智能研究所（DaSCI）、格拉纳达大学）；Department of Information Science and Media Studies, University of Bergen（信息科学与媒体研究系、卑尔根大学）；Data Science and Computational Intelligence Institute (DaSCI), Department of Computer Science and Artificial Intelligence (DCSAI), University of Granada（数据科学与计算智能研究所（DaSCI）、计算机科学与人工智能系（DCSAI）、格拉纳达大学）
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement
Technical University of Munich（慕尼黑工业大学）；Munich Center for Quantum Science and Technology (MCQST)（慕尼黑量子科学与技术中心）；Ruhr-Universität Bochum（波鸿鲁尔大学）；Ludwig-Maximilians-Universität München (LMU Munich)（慕尼黑大学）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Carnegie Mellon University（卡内基梅隆大学）；Partnership on AI（人工智能合作组织）；University of Cambridge（剑桥大学）；Leverhulme CFI（Leverhulme智能未来中心）；University of Texas at Austin（德克萨斯大学奥斯汀分校）；The Alan Turing Institute（艾伦图灵研究所）；Fiddler Labs；Baidu（百度）；CognitiveScale；IBM Research（IBM研究院）
Uncertainty Quantification as a Principled Foundation for Explainable Artificial Intelligence: A Case Study of Counterfactual Explanations
Department of Informatics, USI Lugano（乌里大学信息学院）；Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Institute of Informatics, LMU Munich（慕尼黑大学信息研究所）
The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies
Explainability for Large Language Models: A Survey
New Jersey Institute of Technology（新泽西理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Wake Forest University（维克森林大学）；University of Georgia（佐治亚大学）；Shanghai Jiao Tong University（上海交通大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）；Baidu Inc.（百度公司）；Rice University（莱斯大学）
