Explanation-Based Runtime Verification for Trustworthy ML-driven Optical Networks

2026-07-22

AI Summary

This work addresses the critical challenge of unreliable decision-making in machine learning–driven optical networks, which can severely degrade service quality and system stability. To mitigate this risk, the paper proposes a runtime decision validation method grounded in explainable artificial intelligence (XAI). Prior to executing control actions, the approach generates explanations by analyzing feature importance and interaction patterns, then assesses decision credibility based on the coherence of these explanations and their consistency with underlying physical principles. As the first application of XAI to runtime verification in optical networks, the method effectively intercepts a substantial number of erroneous decisions in transmission quality classification tasks while preserving a high degree of automation, thereby significantly enhancing overall system reliability.

Abstract

Machine learning (ML) models are increasingly integrated into optical network automation frameworks to support tasks such as failure management, performance monitoring and resource allocation. In these environments, ML-driven predictions may be directly coupled with control-plane actions where incorrect decisions can immediately impact service quality, resource efficiency, and network stability. As automation levels increase, ensuring the reliability of individual decisions at deployment time becomes a critical requirement. Explainable artificial intelligence (XAI) techniques have emerged to improve transparency by highlighting the factors influencing ML predictions. In addition to identifying influential features, they provide insights into the underlying reasoning process of the model, revealing how different input variables contribute to the final outcome and how feature interactions shape the decision boundary. In this work, we introduce explanation-based runtime verification, an approach that exploits model explanations to assess the soundness of individual ML decisions before they are executed in the network control loop. The proposed approach evaluates explanation coherence and physics grounding consistency at runtime, enabling the system to defer or reject decisions flagged as uncertain. We demonstrate the effectiveness of our approach on a representative use case of lightpath quality of transmission classification. Experimental results show that explanation-based verification can intercept a significant fraction of erroneous decisions while preserving high automation rate.

Problem

• runtime verification
• trustworthy ML
• optical networks
• decision reliability
• explainable AI

Innovation

• explanation-based runtime verification
• explainable AI
• trustworthy ML
• optical network automation
• decision validation