Explainable AI for Critical Infrastructure Monitoring and Control

Main Article Content

Susan Reynolds
James Nolan

Abstract

Explainable AI (XAI) has emerged as a pivotal paradigm in the domain of critical infrastructure monitoring and control, offering transparency, interpretability, and trustworthiness in AI-driven decision-making processes. In this abstract, we explore the significance of XAI in enhancing the resilience and reliability of critical infrastructure systems, which encompass vital sectors such as energy, transportation, water supply, and telecommunications. We delve into the challenges posed by the deployment of complex AI models in mission-critical environments, where the interpretability of AI-driven insights is paramount for informed decision-making and system oversight. The abstract highlights the key principles and methodologies of XAI tailored to the context of critical infrastructure monitoring and control. We discuss the importance of model transparency, post-hoc explanation techniques, and human-machine collaboration in ensuring the comprehensibility and trustworthiness of AI-generated recommendations and predictions. Furthermore, we examine the role of XAI in facilitating regulatory compliance, risk assessment, and incident response in the event of system failures or anomalies. Moreover, the abstract elucidates the practical applications of XAI in critical infrastructure domains, including anomaly detection, fault diagnosis, predictive maintenance, and situational awareness. We showcase case studies and real-world examples where XAI techniques empower operators, engineers, and decision-makers to understand, validate, and act upon AI-derived insights effectively. In conclusion, Explainable AI for Critical Infrastructure Monitoring and Control represents a crucial enabler for enhancing the resilience, reliability, and safety of essential services that underpin modern society. By fostering transparency, interpretability, and human-centric design principles in AI systems, XAI empowers stakeholders to make informed decisions, mitigate risks, and ensure the continuous operation of critical infrastructure assets in the face of evolving threats and uncertainties.

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How to Cite
Reynolds, S., & Nolan, J. (2025). Explainable AI for Critical Infrastructure Monitoring and Control. ITSI Transactions on Electrical and Electronics Engineering, 12(2), 18–23. Retrieved from https://journals.mriindia.com/index.php/itsiteee/article/view/152
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