De Bock, Koen W., Coussement, Kristof, Caigny, Arno De, Słowiński, Roman, Baesens, Bart, Boute, Robert N., Choi, Tsan-Ming, Delen, Dursun, Kraus, Mathias, Lessmann, Stefan, Maldonado, Sebastián, Martens, David, Óskarsdóttir, María, Vairetti, Carla, Verbeke, Wouter and Weber, Richard (2024) Explainable AI for operational research: a defining framework, methods, applications, and a research agenda. European Journal of Operational Research, 317 (2), 249-272. (doi:10.1016/j.ejor.2023.09.026).
Abstract
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.
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