When 5G began its commercialisation journey around 2020, the discussion on the vision of 6G also surfaced. Researchers expect 6G to have higher bandwidth, coverage, reliability, energy efficiency, lower latency, and an integrated "human-centric" network system powered by artificial intelligence (AI). Such a 6G network will lead to an excessive number of automated decisions made in real-time. These decisions can range widely, from network resource allocation to collision avoidance for self-driving cars. However, the risk of losing control over decision-making may increase due to high-speed, data-intensive AI decision-making beyond designers' and users' comprehension. The promising explainable AI (XAI) methods can mitigate such risks by enhancing the transparency of the black-box AI decision-making process. This paper surveys the application of XAI towards the upcoming 6G age in every aspect, including 6G technologies (e.g., intelligent radio, zero-touch network management) and 6G use cases (e.g., industry 5.0). Moreover, we summarised the lessons learned from the recent attempts and outlined important research challenges in applying XAI for 6G in the near future.

翻译：当5G于2020年左右开始商业化进程时，关于6G愿景的讨论也随之浮现。研究人员预期6G将具备更高的带宽、覆盖率、可靠性、能效、更低的延迟，以及由人工智能驱动的集成化"以人为本"网络系统。这种6G网络将导致大量自动化决策的实时执行，这些决策范围广泛，从网络资源分配到自动驾驶汽车的避障功能。然而，由于高速、数据密集型的人工智能决策超出了设计者与用户的理解范畴，可能增加对决策过程失控的风险。具有前景的可解释人工智能方法通过增强黑箱式AI决策过程的透明度，可有效降低此类风险。本文全面调研了XAI在即将到来的6G时代中的应用，涵盖6G技术（如智能无线电、零接触网络管理）及6G用例（如工业5.0）。此外，我们总结了近期尝试中的经验教训，并概述了在近期将XAI应用于6G时面临的重要研究挑战。