Explainable AI for Transparent Safety Decisions in Intelligent Safety Instrumented Systems

Abstract

The industrial safety systems of today use Artificial Intelligence (AI) to provide advanced functions that enable users to forecast outcomes and make decisions without human intervention. The opaque characteristics of intricate AI models present considerable hurdles in safety-critical contexts where transparency, reliability, and accountability are paramount. The research develops an academic framework which uses Explainable Artificial Intelligence (XAI) to support Intelligent Safety Instrumented Systems (SIS) in creating understandable yet dependable safety assessments. The system establishes operational dependability through its combination of AI-based decision processes with three diagnostic levels and explanation tools and human-operated validation methods. The proposed safety decision-making process allows operators to understand better and meets regulatory requirements while building their trust through explainability which extends across all decision-making steps. The research demonstrates how XAI technology transforms traditional Safety Instrumented Systems into modern industrial systems which provide transparency and better user experience.