Quality-of-Trust in 6G: combining emotional and physical trust through explainable AI

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dc.contributor.authorLi, Chen
dc.contributor.authorQi, Weijie
dc.contributor.authorJin, Bailu
dc.contributor.authorDemestichas, Panagiotis
dc.contributor.authorTsagkaris, Kostas
dc.contributor.authorKritikou, Yiouli
dc.contributor.authorGuo, Weisi
dc.date.accessioned2024-02-19T14:33:54Z
dc.date.available2024-02-19T14:33:54Z
dc.date.issued2023-12-11
dc.description.abstractWireless networks like many multi-user services have to balance limited resources in real-time. In 6G, increased network automation makes consumer trust crucial. Trust is reflect in both a personal emotional sentiment as well as a physical understanding of the transparency of AI decision making. Whilst there has been isolated studies of consumer sentiment to wireless services, this is not well linked to the decision making engineering. Likewise, limited recent research in explainable AI (XAI) has not established a link to consumer perception.Here, we develop a Quality-of-Trust (QoT) KPI that balances personal perception with the quality of decision explanation. That is to say, the QoT varies with both the time-varying sentiment of the consumer as well as the accuracy of XAI outcomes. We demonstrate this idea with an example in Neural Water-Filling (N-WF) power allocation, where the channel capacity is perceived by artificial consumers that communicate through Large Language Model (LLM) generated text feedback. Natural Language Processing (NLP) analysis of emotional feedback is combined with a physical understanding of N-WF decisions via meta-symbolic XAI. Combined they form the basis for QoT. Our results show that whilst the XAI interface can explain up to 98.9% of the neural network decisions, a small proportion of explanations can have large errors causing drops in QoT. These drops have immediate transient effects in the physical mistrust, but emotional perception of consumers are more persistent. As such, QoT tends to combine both instant physical mistrust and long-term emotional trends.en_UK
dc.description.sponsorshipWe acknowledge funding from EC H2020 (778305), and EPSRC (EP/X040518/1)en_UK
dc.identifier.citationLi C, Qi W, Jin B, et al., (2023) Quality-of-Trust in 6G: combining emotional and physical trust through explainable AI. In 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), 10-13 October 2023, Hong Kongen_UK
dc.identifier.eisbn979-8-3503-2928-5
dc.identifier.eissn2577-2465
dc.identifier.isbn979-8-3503-2929-2
dc.identifier.issn1090-3038
dc.identifier.urihttps://doi.org/10.1109/VTC2023-Fall60731.2023.10333364
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/20828
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectmachine learningen_UK
dc.subjectdeep learningen_UK
dc.subjectXAIen_UK
dc.subjectwirelessen_UK
dc.subjecttrusten_UK
dc.subjectsentimenten_UK
dc.subjectNLPen_UK
dc.subjectLLMen_UK
dc.titleQuality-of-Trust in 6G: combining emotional and physical trust through explainable AIen_UK
dc.typeConference paperen_UK

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