Li, ChenGuo, WeisiSun, Schyler ChengyaoAl-Rubaye, SabaTsourdos, Antonios2021-01-222021-01-222020-09-30Chen L, Guo W, Sun SC, et al., (2020) Trustworthy deep learning in 6G-enabled mass autonomy: from concept to quality-of-trust key performance indicators. IEEE Vehicular Technology Magazine, Volume 15, Issue 4, December 2020, pp. 112-1211556-6072https://doi.org/10.1109/MVT.2020.3017181https://dspace.lib.cranfield.ac.uk/handle/1826/16237Mass autonomy promises to revolutionize a wide range of engineering, service, and mobility industries. Coordinating complex communication among hyperdense autonomous agents requires new artificial intelligence (AI)-enabled orchestration of wireless communication services beyond 5G and 6G mobile networks. In particular, safety and mission-critical tasks will legally require both transparent AI decision processes and quantifiable quality-of-trust (QoT) metrics for a range of human end users (consumer, engineer, and legal). We outline the concept of trustworthy autonomy for 6G, including essential elements such as how explainable AI (XAI) can generate the qualitative and quantitative modalities of trust. We also provide XAI test protocols for integration with radio resource management and associated key performance indicators (KPIs) for trust. The research directions proposed will enable researchers to start testing existing AI optimization algorithms and develop new ones with the view that trust and transparency should be built in from the design through the testing phase.enAttribution-NonCommercial 4.0 InternationalMachine LearningDeep LearningTrustXAI6GMass AutonomyArticle