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

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dc.contributor.authorDe, Bock, K.W.
dc.contributor.authorCoussement, K.
dc.contributor.authorCaigny, A.D.
dc.contributor.authorS?owi?ski, R.
dc.contributor.authorBaesens, B.
dc.contributor.authorBoute, R.N.
dc.contributor.authorChoi T.-M.
dc.date.accessioned2024-05-19T14:33:48Z
dc.date.available2024-05-19T14:33:48Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThe 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. © 2023 Elsevier B.V.en_US
dc.description.sponsorship952215; AFB220003; Horizon 2020 Framework Programme, H2020; European Commission, EC: 822214; European Commission, EC; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT: 11200007, 1200221, 1221562, IT23I0061; Fondo Nacional de Desarrollo Científico y Tecnológico, FONDECYT; Horizon 2020en_US
dc.description.sponsorshipThe authors acknowledge all researchers who, through their work, have advocated and accelerated the adoption of (explainable) analytics in OR. The research of Roman Slowi?ski was supported by TAILOR, a project funded by the EU Horizon 2020 (research and innovation funding) programme (EC GA number 952215 ). Sebastián Maldonado, Carla Vairetti, and Richard Weber acknowledge financial support from FONDECYT Chile (Grants 1200221, 11200007, and 1221562), Fondef (IT23I0061), ANID PIA/PUENTE (AFB220003), and NeEDS, a project funded by the EU Horizon 2020 programme (EC GA number 822214 ).en_US
dc.identifier.doi10.1016/j.ejor.2023.09.026
dc.identifier.issn0377-2217
dc.identifier.scopus2-s2.0-85173246691en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.ejor.2023.09.026
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4340
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier B.V.en_US
dc.relation.ispartofEuropean Journal of Operational Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDecision Analysisen_US
dc.subjectExplainable Artificial İntelligenceen_US
dc.subjectInterpretable Machine Learningen_US
dc.subjectXaıen_US
dc.subjectXaıoren_US
dc.titleExplainable AI for Operational Research: A defining framework, methods, applications, and a research agendaen_US
dc.typeArticleen_US

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