De, Bock, K.W.Coussement, K.Caigny, A.D.S?owi?ski, R.Baesens, B.Boute, R.N.Choi T.-M.2024-05-192024-05-1920230377-2217https://doi.org/10.1016/j.ejor.2023.09.026https://hdl.handle.net/20.500.12713/4340The 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. © 2023 Elsevier B.V.eninfo:eu-repo/semantics/openAccessDecision AnalysisExplainable Artificial İntelligenceInterpretable Machine LearningXaıXaıorArticle2-s2.0-8517324669110.1016/j.ejor.2023.09.026Q1