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

European Journal of Operational Research Elsevier

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

Mathias Kraus Juniorprofessur für Data Analytics

Involved external institutions

Université de Lille (ULille, UDL) FR France (FR) University of Antwerp / Universiteit Antwerpen BE Belgium (BE) Humboldt-Universität zu Berlin DE Germany (DE) Oklahoma State University US United States (USA) (US) Reykjavik University IS Iceland (IS) Poznan University of Technology PL Poland (PL) Universidad de Chile CL Chile (CL) Audencia Business School FR France (FR) The University of Liverpool GB United Kingdom (GB) Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven BE Belgium (BE) Universidad de los Andes CL 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://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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