Explanation-Guided Deep Reinforcement Learning for Trustworthy 6G RAN Slicing

Transparency in AI-driven networks is critical for user trust: The paper identifies that despite deep reinforcement learning's strong performance in managing 6G network resources, the "lack of transparency, interpretability, and opaque model concerns" prevents users from trusting AI decisions—a key barrier to adoption of AI in critical infrastructure.

Explainable AI can make complex network decisions understandable: The proposed eXplainable Reinforcement Learning (XRL) scheme uses interpretable reward structures to help AI agents learn optimal decisions while remaining transparent about why specific resource allocation choices are made, addressing the "black-box" problem in traditional deep reinforcement learning.

Interpretability through reward design improves accountability: Rather than adding explanations after the fact, the framework builds interpretability into the core learning process through "intrinsic interpretability of the reward hypothesis," meaning the AI's decision-making logic is understandable from the ground up rather than retrofitted.

Responsible AI matters for high-stakes services: The paper emphasizes that transparency becomes "even more pronounced" when provisioning "highly reliable and secure services," suggesting that explainability requirements should be strongest for critical applications like telecommunications infrastructure that affect many users.

Multi-agent systems require coordinated trustworthiness: The use of "multi-agent XRL" to allocate radio resources indicates that as AI systems become more distributed and interconnected, maintaining explainability across multiple coordinating agents becomes essential for responsible deployment.