Explainable Analytics for Operational Research

K. de Bock (), K. Coussement and A. de Caigny
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K. de Bock: Audencia Business School

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Abstract: This paper introduces the feature cluster on "Explainable AI for Operational Research". Its main purpose is to provide summaries for the 15 contributing research papers that were accepted for inclusion in this feature cluster. To guide the presentation of individual contributions, we refer to the XAIOR framework, or Explainable AI for OR, which is presented in a review paper featured in this feature cluster. XAIOR is defined as the conceptualization and application of advanced methods for transforming data into insights that are simultaneously performant, attributable, and responsible for solving OR problems and enhancing decision-making. This paper zooms in on the underlying dimensions of XAIOR linked to three types of analytics, i.e. performance analytics, attributable analytics and responsible analytics. We discuss the feature cluster contributions' linkage to the XAIOR framework. In particular, contributing papers are categorized along along two dimensions depending on whether the research paper introduces a new XAIOR method that is applicable across OR domains, or whether the paper zooms in on XAIOR aspects of a particular OR application field.

Keywords: XAI; XAIOR; explainable artificial intelligence for operational research; interpretable machine learning (search for similar items in EconPapers)
Date: 2024-09-01
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Published in European Journal of Operational Research, 2024, 317 (2), pp.243-248. ⟨10.1016/j.ejor.2024.04.015⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04549059

DOI: 10.1016/j.ejor.2024.04.015

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