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

Author

Listed:
  • 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 De Caigny
  • 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 N 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)

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.

Suggested Citation

  • Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    • Handle: RePEc:hal:journl:hal-04219546
    DOI: 10.1016/j.ejor.2023.09.026
    Note: View the original document on HAL open archive server: https://hal.science/hal-04219546
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