Explainable and Trustworthy AI for Autonomous Networks: A Review of Interpretability, Robustness, Fairness, and Security
Abstract
The increasing integration of Artificial Intelligence into network control and management introduces significant concerns regarding the reliability and trustworthiness of automated decisions. Autonomous networks may use AI models to perform critical tasks such as routing, resource allocation, fault detection, traffic management, and cybersecurity, making transparency and reliability essential. This review investigates the emerging field of Explainable and Trustworthy AI for networking applications. It examines explainability techniques applicable to machine learning and deep learning models, including feature-based explanations, surrogate models, attention mechanisms, counterfactual explanations, and model-specific interpretation methods. The paper further analyzes robustness against adversarial attacks, data poisoning, model manipulation, distribution shifts, and unexpected network conditions. Privacy, fairness, accountability, and human oversight are also considered. Particular attention is given to the challenges of applying explainable AI to real-time networking systems where computational overhead and latency are critical. The review identifies gaps in existing evaluation frameworks and proposes research directions for developing transparent, robust, secure, and accountable AI-driven autonomous networks.
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