Explainable AI (XAI) for Decision Transparency in Automated Industrial Operations

Abstract

As industrial operations grow increasingly reliant on artificial intelligence (AI) for automation and optimization, the need for transparency in AI-driven decision-making becomes critical. Traditional black-box models often lack interpretability, creating trust issues, safety concerns, and regulatory challenges. Explainable AI (XAI) seeks to address these concerns by providing human-understandable justifications for AI decisions. This paper explores the role of XAI in enhancing decision transparency within automated industrial environments. We present an overview of XAI techniques, evaluate their applicability in various industrial scenarios such as predictive maintenance, quality control, and autonomous process management, and discuss how explainability impacts stakeholder trust, compliance, and operational safety. We also propose a framework for integrating XAI into industrial AI systems, emphasizing real-time interpretability and human-in-the-loop design. The findings underscore that explainable AI not only enhances transparency but also drives responsible and sustainable adoption of AI in industry.