Explainable reinforcement and causal learning for improving trust to 6G stakeholders

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dc.contributor.authorArana-Catania, Miguel
dc.contributor.authorSonee, Amir
dc.contributor.authorKhan, Abdul-Manan
dc.contributor.authorFatehi, Kavan
dc.contributor.authorTang, Yun
dc.contributor.authorJin, Bailu
dc.contributor.authorSoligo, Anna
dc.contributor.authorBoyle, David
dc.contributor.authorCalinescu, Radu
dc.contributor.authorYadav, Poonam
dc.contributor.authorAhmadi, Hamed
dc.contributor.authorTsourdos, Antonios
dc.contributor.authorGuo, Weisi
dc.contributor.authorRusso, Alessandra
dc.date.accessioned2025-06-09T09:48:02Z
dc.date.available2025-06-09T09:48:02Z
dc.date.freetoread2025-06-09
dc.date.issued2025-06-01
dc.date.pubOnline2025-04-22
dc.description.abstractFuture telecommunications will increasingly integrate AI capabilities into network infrastructures to deliver seamless and harmonized services closer to end-users. However, this progress also raises significant trust and safety concerns. The machine learning systems orchestrating these advanced services will widely rely on deep reinforcement learning (DRL) to process multi-modal requirements datasets and make semantically modulated decisions, introducing three major challenges: (1) First, we acknowledge that most explainable AI research is stakeholder-agnostic while, in reality, the explanations must cater for diverse telecommunications stakeholders, including network service providers, legal authorities, and end users, each with unique goals and operational practices; (2) Second, DRL lacks prior models or established frameworks to guide the creation of meaningful long-term explanations of the agent's behaviour in a goal-oriented RL task, and we introduce state-of-the-art approaches such as reward machine and sub-goal automata that can be universally represented and easily manipulated by logic programs and verifiably learned by inductive logic programming of answer set programs; (3) Third, most explainability approaches focus on correlation rather than causation, and we emphasise that understanding causal learning can further enhance 6G network optimisation. Together, in our judgement they form crucial enabling technologies for trustworthy services in 6G. This review offers a timely resource for academic researchers and industry practitioners by highlighting the methodological advancements needed for explainable DRL (X-DRL) in 6G. It identifies key stakeholder groups, maps their needs to X-DRL solutions, and presents case studies showcasing practical applications. By identifying and analysing these challenges in the context of 6G case studies, this work aims to inform future research, transform industry practices, and highlight unresolved gaps in this rapidly evolving field.
dc.description.journalNameIEEE Open Journal of the Communications Society
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)
dc.description.sponsorshipEPSRC CHEDDAR: Communications Hub for Empowering Distributed Cloud Computing Applications and Research, EP/X040518/1 and EP/Y037421/1
dc.format.extentpp. 4101-4125
dc.identifier.citationArana-Catania M, Sonee A, Khan A-M, et al., (2025) Explainable reinforcement and causal learning for improving trust to 6G stakeholders. IEEE Open Journal of the Communications Society, Volume 6, June 2025, pp. 4101-4125en_UK
dc.identifier.eissn2644-125X
dc.identifier.elementsID672910
dc.identifier.issn2644-125X
dc.identifier.urihttps://doi.org/10.1109/ojcoms.2025.3563415
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23994
dc.identifier.volumeNo6
dc.language.isoen
dc.publisherIEEE en_UK
dc.publisher.urihttps://ieeexplore.ieee.org/document/10973290
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject6G mobile communication en_UK
dc.subjectArtificial intelligence en_UK
dc.subjectStakeholders en_UK
dc.subjectExplainable AI en_UK
dc.subjectReviews en_UK
dc.subjectDecision making en_UK
dc.subjectOptimization en_UK
dc.subjectOpen RAN en_UK
dc.subjectVehicle dynamics en_UK
dc.subjectTelecommunications en_UK
dc.subject6G en_UK
dc.subjectreinforcement learning en_UK
dc.subjecttrust en_UK
dc.subjectstakeholders en_UK
dc.subjectcausal learning en_UK
dc.subject4006 Communications Engineering en_UK
dc.subject40 Engineering en_UK
dc.subjectMachine Learning and Artificial Intelligence en_UK
dc.subjectGeneric health relevance en_UK
dc.titleExplainable reinforcement and causal learning for improving trust to 6G stakeholders en_UK
dc.typeArticle
dc.type.subtypeReview
dcterms.dateAccepted2025-04-20

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