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

Wireless 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.We acknowledge funding from EC H2020 (778305), and EPSRC (EP/X040518/1

Final report on dissemination, regulation, standardization, exploitation & training : D6.3

In D6.1 deliverable project dissemination, exploitation and training plans, as well as standardization & regulatory approach strategy was presented. The D6.2 reported on the necessary updates of these strategies and the actions taken by the partners in line with them, as well as the obtained results. In this D6.3 deliverable, a full set of project dissemination activities, standardization & regulatory contributions as well as an operator’s “cook book” outlining steps necessary for full deployment of ON functionality and services, are presented.Deliverable D6.3 del projecte OneFITPostprint (author’s final draft

Business scenarios, technical challenges and system requirements - D2.1

Deliverable D2.1 del projecte Europeu OneFIT (ICT-2009-257385)Preprin

Formulation, implementation considerations, and first performance evaluation of algorithmic solutions - D4.1

Deliverable D4.1 del projecte Europeu OneFIT (ICT-2009-257385)This deliverable contains a first version of the algorithmic solutions for enabling opportunistic networks. The presented algorithms cover the full range of identified management tasks: suitability, creation, QoS control, reconfiguration and forced terminations. Preliminary evaluations complement the proposed algorithms. Implementation considerations towards the practicality of the considered algorithms are also included.Preprin

Results analysis and validation - D5.3

Deliverable D5.3 del projecte OneFITPostprint (author’s final draft