人类-人工智能系统中的解释：可解释AI的文献综述、关键思想和出版物概要以及参考书目
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI
摘要：
这是一个综合性的评论，解决了这个问题，“什么是一个好的解释？“关于人工智能系统。相关文献浩如烟海。因此，这种审查必然是有选择性的。尽管如此，本报告阐述了大多数关键概念和问题。该报告概括了计算机科学努力创建解释和指导系统（智能辅导系统和专家系统）的历史。该报告表达了现代人工智能中的可解释性问题和挑战，并提出了主要心理学解释理论的胶囊观点。某些文章因其与XAI的特殊相关性而脱颖而出，并突出了其方法，结果和关键点。建议鼓励AI/XAI研究人员在他们的研究报告中包括他们的经验或实验方法的更详细的细节，以实验心理学研究报告的方式：参与者，说明，程序，任务，因变量（测量和度量的操作定义），自变量（条件）和控制条件的细节。
英文摘要：
This is an integrative review that address the question, "What makes for a good explanation?" with reference to AI systems. Pertinent literatures are vast. Thus, this review is necessarily selective. That said, most of the key concepts and issues are expressed in this Report. The Report encapsulates the history of computer science efforts to create systems that explain and instruct (intelligent tutoring systems and expert systems). The Report expresses the explainability issues and challenges in modern AI, and presents capsule views of the leading psychological theories of explanation. Certain articles stand out by virtue of their particular relevance to XAI, and their methods, results, and key points are highlighted. It is recommended that AI/XAI researchers be encouraged to include in their research reports fuller details on their empirical or experimental methods, in the fashion of experimental psychology research reports: details on Participants, Instructions, Procedures, Tasks, Dependent Variables (operational definitions of the measures and metrics), Independent Variables (conditions), and Control Conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
