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

Koen W. de Bock (), Kristof Coussement (), Arno Caigny, Roman Slowiński, Bart Baesens, Robert Boute, Tsan-Ming Choi, Dursun Delen, Mathias Kraus, Stefan Lessmann, Sebastián Maldonado, David Martens, María Óskarsdóttir, Carla Vairetti, Wouter Verbeke and Richard Weber
Additional contact information
Koen W. de Bock: Audencia Business School
Kristof Coussement: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique, IÉSEG School Of Management [Puteaux]
Arno Caigny: LEM - Lille économie management - UMR 9221 - UA - Université d'Artois - UCL - Université catholique de Lille - Université de Lille - CNRS - Centre National de la Recherche Scientifique
Roman Slowiński: Poznan University of Technology, Systems Research Institute of the Polish Academy of Sciences
Bart Baesens: KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven, SBS - Southampton Business School
Robert Boute: Vlerick Business School [Leuven], KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
Tsan-Ming Choi: University fo Liverpool Management School
Dursun Delen: Spears School of Business (Oklahoma State University), Istinye University
Mathias Kraus: FAU - Friedrich-Alexander Universität Erlangen-Nürnberg = University of Erlangen-Nuremberg
Stefan Lessmann: HU Berlin - Humboldt-Universität zu Berlin = Humboldt University of Berlin = Université Humboldt de Berlin
Sebastián Maldonado: UCHILE - Universidad de Chile = University of Chile [Santiago], ISCI - Instituto de Sistemas Complejos de Ingeniería
David Martens: UA - University of Antwerp
María Óskarsdóttir: Reykjavík University
Carla Vairetti: UANDES - Universidad de los Andes [Santiago]
Wouter Verbeke: KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
Richard Weber: UCHILE - Universidad de Chile = University of Chile [Santiago], ISCI - Instituto de Sistemas Complejos de Ingeniería

Post-Print from HAL

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.

Keywords: Decision analysis; XAI; explainable artificial intelligence; interpretable machine learning; XAIOR (search for similar items in EconPapers)
Date: 2023-09-01
New Economics Papers: this item is included in nep-ain and nep-cmp
Note: View the original document on HAL open archive server: https://hal.science/hal-04219546v1
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Published in European Journal of Operational Research, 2023, 137 (2), pp.249-272. ⟨10.1016/j.ejor.2023.09.026⟩

Downloads: (external link)
https://hal.science/hal-04219546v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04219546

DOI: 10.1016/j.ejor.2023.09.026

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().