Intent-based End-to-End Explainability Orchestration Framework for AI-Native Networks

The advent of 6G networks is driving a paradigm shift toward AI-native infrastructures, embedding artificial intelligence deeply across multiple operational layers. This evolution necessitates transparency, trustworthiness, and explainability to foster reliable and accountable network decisions. Explainable AI (XAI) techniques have emerged as crucial tools to ensure that AI-driven network operations are interpretable and aligned with human expectations, thus addressing inherent reliability issues like model degradation and risks associated with large language models (LLMs), including hallucinations. Complementing these advancements, Intent-Driven Management (IDM), standardized by 3GPP, enables translation of high-level user intents into actionable, automated network decisions, enhancing flexibility, efficiency, and resilience. This paper presents a comprehensive end-to-end (E2E) Explainability Orchestration framework integrated across resource, service, and business levels, guided by an intent-based approach. Aligned with ITU-R and 3GPP SA1 visions, the proposed AI-native orchestration framework leverages real-time analytics, intent-driven automation, and integrated explainability to ensure network transparency, regulatory compliance, and trustworthiness in next-generation telecommunication ecosystems.