Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda
本文提出了一个规范框架来定义运筹学中的可解释人工智能（XAIOR），涵盖性能、归因和责任三个维度，并综述了不同领域和应用的部署方法，最后给出了未来研究议程。
The ability to understand and explain the outcomes of data analysis methods, with regard to aiding decision-making, has become a critical requirement for many applications. For example, in operational research domains, data analytics have long been promoted as a way to enhance decision-making. This study proposes a comprehensive, normative framework to define explainable artificial intelligence (XAI) for operational research (XAIOR) as a reconciliation of three subdimensions that constitute its requirements: performance, attributable, and responsible analytics. In turn, this article offers in-depth overviews of how XAIOR can be deployed through various methods with respect to distinct domains and applications. Finally, an agenda for future XAIOR research is defined.