Explainable AI (XAI) Models for Transparent Decision Making in IIoT

Abstract

The integration of Artificial Intelligence (AI) into the Industrial Internet of Things (IIoT) has enabled predictive analytics, autonomous control, and optimized operations. However, the increasing reliance on complex and opaque machine learning models raises concerns regarding trust, accountability, and regulatory compliance in critical industrial environments. Explainable AI (XAI) aims to address these concerns by providing transparent and interpretable decision-making processes. This paper explores the intersection of XAI and IIoT, highlighting the challenges of applying explainable models in real-time, data-intensive industrial contexts. We survey existing XAI techniques and evaluate their suitability for IIoT applications, such as predictive maintenance, quality assurance, and anomaly detection. Additionally, we discuss evaluation metrics, present case studies, and propose a framework for integrating XAI into IIoT pipelines. Our findings demonstrate the potential of XAI to enhance transparency, user trust, and operational safety in next-generation industrial systems.