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Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda

Author

Listed:
  • 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
  • Weber, Richard

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

  • 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, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.
    • Handle: RePEc:eee:ejores:v:317:y:2024:i:2:p:249-272
    DOI: 10.1016/j.ejor.2023.09.026
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