  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.
