Knowledge-powered Explainable Artificial Intelligence (XAI) for Network Automation Towards 6G

AI systems managing networks need to be explainable: The paper identifies that current deep learning models used for network automation lack explainability, making it difficult for human operators to understand why the AI made specific decisions. This is critical for responsible deployment since network failures could affect millions of users.

Knowledge-powered AI can improve trustworthiness: The proposed framework combines AI with explicit knowledge to create decisions that humans can understand, addressing a key barrier to responsible AI adoption in critical infrastructure. This approach helps bridge the gap between AI capability and human oversight.

One-size-fits-all AI models don't work for networks: The paper highlights that deep learning models trained for one network often cannot be applied to different networks or settings, meaning AI systems need to be customized and validated for specific contexts rather than deployed universally—an important consideration for responsible implementation.

Network automation is still emerging: The authors note that "research on network automation is still in its infancy," suggesting that responsible AI governance frameworks for this domain are still being developed. This presents an opportunity to build responsible practices into network automation from the ground up rather than retrofitting them later.

Human oversight remains essential: By emphasizing the need for "human-understandable" AI agents, the paper reinforces that human operators should retain the ability to comprehend and potentially override AI decisions in critical network management scenarios.