Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation

Black-box AI systems pose real risks in critical infrastructure: The paper demonstrates that Deep Reinforcement Learning, while effective at optimizing complex network tasks, creates a "trust gap" that prevents deployment in carrier-grade 5G networks because operators cannot understand or audit how decisions are made—a significant barrier to responsible AI adoption in essential services.

Interpretability doesn't require sacrificing performance: DeRAN achieves 78-87% of the original AI system's performance while making decisions fully transparent and auditable, suggesting that the common trade-off between "powerful but opaque" versus "weak but explainable" AI may be a false choice when proper techniques are applied.

Symbolic AI offers a practical path to trustworthy automation: By translating complex neural network decisions into human-readable rules and logic, the framework enables network operators to understand why decisions are made, verify they're correct, and maintain meaningful human oversight—addressing a key requirement for responsible AI in critical infrastructure.

Concept-driven abstraction is key to meaningful explanations: Rather than explaining raw data patterns, DeRAN first translates network telemetry into semantically meaningful concepts that operators already understand, demonstrating that effective AI transparency requires domain expertise and human-centered design, not just technical explanation methods.

Live testing validates real-world applicability: The framework was tested on an actual 5G testbed rather than simulations alone, providing evidence that interpretable AI systems can work in production environments—an important validation for responsible AI deployment beyond research settings.