Building trust through explainable AI in adaptive optical transport networks

Sachin Kumar; Pritibha Sukhroop; Vikas Sharma; Ashok Kumar; Rajni Rani

doi:10.1515/joc-2025-0320

Article

Building trust through explainable AI in adaptive optical transport networks

Sachin Kumar , Pritibha Sukhroop , Vikas Sharma , Ashok Kumar and Rajni Rani

Published/Copyright: September 11, 2025

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From the journal Journal of Optical Communications

Abstract

This paper introduces a novel approach to incorporate XAI in adaptive optical transport systems that aims to increase the interpretability and transparency of AI-based decisions. The research focuses on two key goals: (1) to develop and test how to design interpretable AI models, which can explain their decisions in optical network management tasks, and (2) to measure the effect of explainability on operator trust, fault localization, and overall system performance. The proposed architecture employs well-known XAI methods (e.g., SHAP, LIME, attention-based visualization) combined with supervised and reinforcement learning, for functions’ dynamic bandwidth allocation, path reconfiguration, and for the root-cause failure analysis.

Keywords: explainable AI; optical networking; adaptive optical transport systems

Corresponding author: Vikas Sharma, Department of Electronics and Communication Engineering, Subharti Institute of Technology and Engineering, Swami Vivekanand Subharti University, Meerut, India, E-mail: vikas741@gmail.com

Acknowledgments

Thanks to all my coauthors for the support.

Research ethics: Not applicable.
Informed consent: We all are fully responsible for this paper.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Use of Large Language Models, AI and Machine Learning Tools: None declared.
Conflict of interest: The authors state no conflict of interest.
Research funding: None declared.
Data availability: Not applicable.

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Received: 2025-08-04

Accepted: 2025-08-04

Published Online: 2025-09-11

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https://doi.org/10.1515/joc-2025-0320

Keywords for this article

explainable AI; optical networking; adaptive optical transport systems