Explainable Artificial Intelligence (Xai) for Decision Support in Safety-Critical Information Systems: Enhancing Trust, Transparency, and Reliability

Abstract

Safety-critical information systems, such as those used in healthcare, aviation, nuclear power, and autonomous transportation, demand exceptionally high levels of reliability and accountability in decision-making processes. Traditional artificial intelligence (AI) methods, particularly deep learning and black-box models, offer high performance but often lack interpretability and transparency, limiting their applicability in environments where safety and human oversight are paramount. Explainable Artificial Intelligence (XAI) has emerged as a promising approach to bridge this gap by providing interpretable, understandable, and justifiable AI outputs. This paper examines the role of XAI in enhancing decision support within safety-critical systems, highlighting its significance in fostering trust, ensuring compliance with regulatory standards, and supporting human operators in critical decision-making. We explore various XAI methods, their applications in real-world scenarios, associated challenges, and future research directions to improve both reliability and interpretability in high-stakes environments.

KEYWORDS: Explainable AI, XAI, Safety-Critical Systems, Decision Support, Transparency, Human-AI Collaboration, Trust, Reliability