33De Bock, Koen W.Coussement, KristofCaigny, Arno DeS?owi?ski, RomanBaesens, BartBoute, Robert N.Choi, Tsan MingDelen, DursunKraus, MathiasLessmann, StefanMaldonado, SebastiánMartens, DavidÓskarsdóttir, MaríaVairetti, CarlaVerbeke, WouterWeber, Richard05-01-202605-01-2026333333The 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.33info:eu-repo/semantics/openAccessDecision analysisExplainable artificial intelligenceInterpretable machine learningXAIXAIOR33