Explainable Artificial Intelligence Models for Transparent Decision-Making in High-Risk Domains

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) systems are increasingly deployed in high-risk domains such as healthcare, finance, criminal justice, and autonomous systems. While these models often demonstrate superior predictive performance, their opaque nature raises serious concerns regarding accountability, trust, fairness, and ethical compliance. This paper examines the role of Explainable Artificial Intelligence (XAI) in enhancing transparency and interpretability of AI-driven decisions in high-risk environments. It reviews key explainability techniques, compares model-agnostic and model-specific approaches, and analyzes their applicability across critical domains. The study highlights regulatory and ethical implications and identifies challenges related to accuracy–interpretability trade-offs. The paper argues that explainability is not merely a technical enhancement but a foundational requirement for responsible AI deployment in high-stakes decision-making contexts.