Explainable, Interpretable & Trustworthy AI for Next-Generation Network Autonomy

Prof Murat KARAKUS, Ankara University

Abstract

AI-driven automation is rapidly transforming future networks, distributed infrastructures, and service operations, yet the opaque nature of AI models continues to limit trustworthiness, adoption, and safe decision-making in next-generation network environments. 

This tutorial provides a structured, hands-on introduction to Explainable, Interpretable, and Trustworthy AI (XAI & IAI) for trusted network autonomy, distributed intelligence, operations, and management. We explore the foundations of explainability, including key model-agnostic and model-aware techniques such as LIME, SHAP, Integrated Gradients, surrogate modeling, counterfactual reasoning, and causal approaches. We demonstrate how these techniques enable debugging, assurance, and operator confidence across complex network and distributed edge-cloud ecosystems. This tutorial delivers a focused exploration of XAI theory for network autonomy, operations, and management. It introduces core interpretability concepts and widely used techniques, such as LIME, SHAP, Integrated Gradients, surrogate models, counterfactuals, and causal reasoning, building on seminal XAI research. The session connects these methods to emerging XAI-for-Networks work across O-RAN, 6G, IoT ecosystems, edge-cloud environments, intrusion detection, and knowledge-driven automation, presenting a taxonomy of use cases that span anomaly triage, SLA diagnosis, resource allocation, distributed intelligence, and validation of closedloop actions.

Participants will engage in hands-on demonstrations using synthetic and anonymized KPI/telemetry datasets, exploring explainability challenges across O-RAN, AIOps/MLOps pipelines, distributed and edge-cloud environments, and upcoming regulatory frameworks. The tutorial concludes by outlining future research, including XAI for LLM-based agents, real-time and distributed explainability, and by highlighting how explainability can be systematically embedded into trustworthy autonomous network management. By connecting explainability with autonomy and distributed intelligence, this tutorial equips researchers and operators with the conceptual tools and practical skills to design, evaluate, and deploy trustworthy AI systems for next-generation AI-native networks and distributed infrastructures.