Empowering explainable artificial intelligence through case-based reasoning: a comprehensive exploration

Simple item page
dc.contributor.authorPradeep, Preejaen
dc.contributor.authorCaro-Martínez, Martaen
dc.contributor.authorWijekoon, Anjanaen
dc.contributor.funderEuropean Commissionen
dc.contributor.funderIrish Research Councilen
dc.contributor.funderScience Foundation Irelanden
dc.date.accessioned2025-09-23T09:31:24Z
dc.date.available2025-09-23T09:31:24Z
dc.date.issued2025-09-16en
dc.description.abstractArtificial intelligence (AI) advancements have significantly broadened its application across various sectors, simultaneously elevating concerns regarding the transparency and understandability of AI-driven decisions. Addressing these concerns, this paper embarks on an exploratory journey into Case-Based Reasoning (CBR) and Explainable Artificial Intelligence (XAI), critically examining their convergence and the potential this synergy holds for demystifying the decision-making processes of AI systems. We employ the concept of Explainable CBR (XCBR) system that leverages CBR to acquire case-based explanations or generate explanations using CBR methodologies to enhance AI decision explainability. Though the literature has few surveys on XCBR, recognizing its potential necessitates a detailed exploration of the principles for developing effective XCBR systems. We present a cycle-aligned perspective that examines how explainability functions can be embedded throughout the classical CBR phases: Retrieve, Reuse, Revise, and Retain. Drawing from a comprehensive literature review, we propose a set of six functional goals that reflect key explainability needs. These goals are mapped to six thematic categories, forming the basis of a structured XCBR taxonomy. The discussion extends to the broader challenges and prospects facing the CBR-XAI arena, setting the stage for future research directions. This paper offers design guidance and conceptual grounding for future XCBR research and system development.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationPradeep, P., Caro-Martínez, M. and Wijekoon, A. (2025) ‘Empowering explainable artificial intelligence through case-based reasoning: a comprehensive exploration’, IEEE Transactions on Knowledge and Data Engineering, 37(12), pp. 7120–7139. https://doi.org/10.1109/TKDE.2025.3609825en
dc.identifier.doi10.1109/TKDE.2025.3609825en
dc.identifier.eissn1558-2191en
dc.identifier.endpage7139en
dc.identifier.issn1041-4347en
dc.identifier.issued12
dc.identifier.journaltitleIEEE Transactions on Knowledge and Data Engineeringen
dc.identifier.startpage7120en
dc.identifier.urihttps://hdl.handle.net/10468/17892
dc.identifier.volume37
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/Research Centres Programme::Phase 2/12/RC/2289_P2/IE/INSIGHT_Phase 2 /en
dc.relation.projectinfo:eu-repo/grantAgreement/UKRI/EPSRC/EP/V061755/1/GB/iSee: Intelligent Sharing of Explanation Experience by Users for Users/en
dc.rights© 2025, the Authors. Published by IEEE. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectCase-Based Reasoningen
dc.subjectExplainable Artificial Intelligenceen
dc.subjectHuman-understandable explanationsen
dc.subjectTrustworthy AIen
dc.subjectXCBRen
dc.subject~Computer Science - Journal Articles~en
dc.titleEmpowering explainable artificial intelligence through case-based reasoning: a comprehensive explorationen
dc.typeArticle (peer-reviewed)en
Files
Original bundle
Now showing 1 - 2 of 2
Thumbnail Image
Name:
Empowering_Explainable_Artificial_Intelligence_Through_Case-Based_Reasoning_A_Comprehensive_Exploration.pdf
Size:
1.93 MB
Format:
Adobe Portable Document Format
Description:
Accepted Version
Thumbnail Image
Name:
Empowering_Explainable_Artificial_Intelligence_Through_Case-Based_Reasoning_A_Comprehensive_Exploration.pdf
Size:
1.49 MB
Format:
Adobe Portable Document Format
Description:
Published Version
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
2.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
School of Computer Science and Information Technology
Insight Centre for Data Analytics - Journal Articles