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Article Dans Une Revue European Journal of Operational Research Année : 2023

Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda

Koen W. de Bock
Arno De Caigny
  • Fonction : Auteur

Résumé

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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Dates et versions

hal-04219546 , version 1 (27-09-2023)

Identifiants

Citer

Koen W. de Bock, Kristof Coussement, Arno De Caigny, Roman Slowiński, Bart Baesens, et al.. Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda. European Journal of Operational Research, 2023, ⟨10.1016/j.ejor.2023.09.026⟩. ⟨hal-04219546⟩
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