Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda
Coussement, Kristof Caigny, Arno De Słowiński, Roman Baesens, Bart Boute, Robert Choi, Tsan-Ming Delen, Dursun Kraus, Mathias Lessmann, Stefan Maldonado, Sebastián
Coussement, Kristof
Caigny, Arno De
Słowiński, Roman
Baesens, Bart
Boute, Robert
Choi, Tsan-Ming
Delen, Dursun
Kraus, Mathias
Lessmann, Stefan
Maldonado, Sebastián
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Publication Type
Journal article with impact factor
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Supervisor
Publication Year
2024
Journal
European Journal of Operational Research
Book
Publication Volume
317
Publication Issue
2
Publication Begin page
249
Publication End page
272
Publication NUmber of pages
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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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Keywords
Decision analysis, XAI, Explainable artificial intelligence, Interpretable machine learning, XAIOR