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

Koen W. De Bock; Kristof Coussement; Arno De Caigny; Roman Słowiński; Bart Baesens; Robert N. Boute; Tsan Ming Choi; Dursun Delen; Mathias Kraus; Stefan Lessmann; Sebastián Maldonado; David Martens; María Óskarsdóttir; Carla Vairetti; Wouter Verbeke; Richard Weber

doi:10.1016/j.ejor.2023.09.026

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

Koen W. De Bock^*, Kristof Coussement, Arno De Caigny, Roman Słowiński, Bart Baesens, Robert N. Boute, Tsan Ming Choi, Dursun Delen, Mathias Kraus, Stefan Lessmann, Sebastián Maldonado, David Martens, María Óskarsdóttir, Carla Vairetti, Wouter Verbeke, Richard Weber

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

35 Scopus citations

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.

Original language	English
Pages (from-to)	1-24
Number of pages	24
Journal	European Journal of Operational Research
Volume	317
Issue number	2
DOIs	https://doi.org/10.1016/j.ejor.2023.09.026
State	Published - 1 Sep 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

Decision analysis
Explainable artificial intelligence
Interpretable machine learning
XAI
XAIOR

Access to Document

10.1016/j.ejor.2023.09.026

Cite this

De Bock, K. W., Coussement, K., Caigny, A. D., Słowiński, R., Baesens, B., Boute, R. N., Choi, T. M., Delen, D., Kraus, M., Lessmann, S., Maldonado, S., Martens, D., Óskarsdóttir, M., Vairetti, C., Verbeke, W., & Weber, R. (2024). Explainable AI for Operational Research: a defining framework, methods, applications, and a research agenda. European Journal of Operational Research, 317(2), 1-24. https://doi.org/10.1016/j.ejor.2023.09.026

@article{02a2e80b280749a5afaa5df3ea718aac,

title = "Explainable AI for Operational Research: a defining framework, methods, applications, and a research agenda",

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.",

keywords = "Decision analysis, Explainable artificial intelligence, Interpretable machine learning, XAI, XAIOR",

author = "{De Bock}, {Koen W.} and Kristof Coussement and Caigny, {Arno De} and Roman S{\l}owi{\'n}ski and Bart Baesens and Boute, {Robert N.} and Choi, {Tsan Ming} and Dursun Delen and Mathias Kraus and Stefan Lessmann and Sebasti{\'a}n Maldonado and David Martens and Mar{\'i}a {\'O}skarsd{\'o}ttir and Carla Vairetti and Wouter Verbeke and Richard Weber",

note = "Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.",

year = "2024",

month = sep,

day = "1",

doi = "10.1016/j.ejor.2023.09.026",

language = "English",

volume = "317",

pages = "1--24",

journal = "European Journal of Operational Research",

issn = "0377-2217",

publisher = "Elsevier B.V.",

number = "2",

}

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 2024, 'Explainable AI for Operational Research: a defining framework, methods, applications, and a research agenda', European Journal of Operational Research, vol. 317, no. 2, pp. 1-24. https://doi.org/10.1016/j.ejor.2023.09.026

TY - JOUR

T1 - Explainable AI for Operational Research

T2 - a defining framework, methods, applications, and a research agenda

AU - De Bock, Koen W.

AU - Coussement, Kristof

AU - Caigny, Arno De

AU - Słowiński, Roman

AU - Baesens, Bart

AU - Boute, Robert N.

AU - Choi, Tsan Ming

AU - Delen, Dursun

AU - Kraus, Mathias

AU - Lessmann, Stefan

AU - Maldonado, Sebastián

AU - Martens, David

AU - Óskarsdóttir, María

AU - Vairetti, Carla

AU - Verbeke, Wouter

AU - Weber, Richard

PY - 2024/9/1

Y1 - 2024/9/1

N2 - 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.

AB - 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.

KW - Decision analysis

KW - Explainable artificial intelligence

KW - Interpretable machine learning

KW - XAI

KW - XAIOR

UR - http://www.scopus.com/inward/record.url?scp=85173246691&partnerID=8YFLogxK

U2 - 10.1016/j.ejor.2023.09.026

DO - 10.1016/j.ejor.2023.09.026

M3 - Review article

AN - SCOPUS:85173246691

SN - 0377-2217

VL - 317

SP - 1

EP - 24

JO - European Journal of Operational Research

JF - European Journal of Operational Research

IS - 2

ER -

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

Abstract

Bibliographical note

Keywords

Access to Document

Other files and links

Fingerprint

Cite this