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 › Article › peer-review

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
Journal	European Journal of Operational Research
DOIs	https://doi.org/10.1016/j.ejor.2023.09.026
State	Accepted/In press - 2023

Bibliographical note

Funding Information:
The authors acknowledge all researchers who, through their work, have advocated and accelerated the adoption of (explainable) analytics in OR. The research of Roman Slowiński 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 ).

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

Decision analysis
Explainable artificial intelligence
Interpretable machine learning
XAI
XAIOR

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10.1016/j.ejor.2023.09.026

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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. (Accepted/In press). 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

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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 = "Funding Information: The authors acknowledge all researchers who, through their work, have advocated and accelerated the adoption of (explainable) analytics in OR. The research of Roman Slowi{\'n}ski was supported by TAILOR, a project funded by the EU Horizon 2020 (research and innovation funding) programme (EC GA number 952215 ). Sebasti{\'a}n 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 ). Publisher Copyright: {\textcopyright} 2023 Elsevier B.V.",

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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, '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

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

N1 - Funding Information: The authors acknowledge all researchers who, through their work, have advocated and accelerated the adoption of (explainable) analytics in OR. The research of Roman Slowiński 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 ). Publisher Copyright: © 2023 Elsevier B.V.

PY - 2023

Y1 - 2023

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

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U2 - 10.1016/j.ejor.2023.09.026

DO - 10.1016/j.ejor.2023.09.026

M3 - Article

AN - SCOPUS:85173246691

SN - 0377-2217

JO - European Journal of Operational Research

JF - European Journal of Operational Research

ER -

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

Abstract

Bibliographical note

Keywords

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