Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda
De Bock KW, Coussement K, Caigny AD, Słowiński R, Baesens B, Boute RN, Choi TM, Delen D, Kraus M, Lessmann S, Maldonado S, Martens D, Óskarsdóttir M, Vairetti C, Verbeke W, Weber R (2023)
Publication Type: Journal article
Publication year: 2023
Journal
DOI: 10.1016/j.ejor.2023.09.026
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.
Authors with CRIS profile
Involved external institutions
Audencia Business School
France (FR)
Université de Lille (ULille, UDL)
France (FR)
Poznan University of Technology
Poland (PL)
Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven
Belgium (BE)
The University of Liverpool
United Kingdom (GB)
Oklahoma State University
United States (USA) (US)
Humboldt-Universität zu Berlin
Germany (DE)
Universidad de Chile
Chile (CL)
University of Antwerp / Universiteit Antwerpen
Belgium (BE)
Reykjavik University
Iceland (IS)
Universidad de los Andes
Chile (CL)
France (FR)
Université de Lille (ULille, UDL)
France (FR)
Poznan University of Technology
Poland (PL)
Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven
Belgium (BE)
The University of Liverpool
United Kingdom (GB)
Oklahoma State University
United States (USA) (US)
Humboldt-Universität zu Berlin
Germany (DE)
Universidad de Chile
Chile (CL)
University of Antwerp / Universiteit Antwerpen
Belgium (BE)
Reykjavik University
Iceland (IS)
Universidad de los Andes
Chile (CL)
How to cite
APA:
De Bock, K.W., Coussement, K., Caigny, A.D., Słowiński, R., Baesens, B., Boute, R.N.,... Weber, R. (2023). Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda. European Journal of Operational Research. https://dx.doi.org/10.1016/j.ejor.2023.09.026
MLA:
De Bock, Koen W., et al. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda." European Journal of Operational Research (2023).
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