Architecting Explainable Artificial Intelligence Systems for Transparent Reasoning in Safety-Critical Applications
The surging deployment of Artificial Intelligence (AI) systems across safety-critical sectors — namely healthcare diagnostics, autonomous driving, aircraft regulation, and industrial automation — has resulted in a burgeoning need for transparent decision-making frameworks capable of justifiability and accountability. This paper describes a holistic architectural paradigm for constructing Explainable Artificial Intelligence (XAI) systems which provide sufficient reasoning transparency in mission-critical settings, where the failure of a system may have disastrous outcomes. We examine the limitations of black-box AI today and introduce a layered explainability framework that merges post-hoc explanation methods with attention processes and methodologies for synthetic formation of human personified I/O logic rules. It builds on SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations) and counterfactual reasoning to provide fine-grained, context-aware explanations for model predictions. We also align with regulatory compliance needs, including the EU AI Act and FDA guidelines, making explainability an integral part of our design process rather than a standalone consideration. We conduct extensive experimental evaluations over medical imaging, autonomous driving, and fault detection datasets, showcasing that our architecture yields comparable predictive accuracy while substantially improving interpretability scores. In summary, this work addresses the key challenge of the disparity between AI performance and human trust by providing a solid underpinning for responsible deployment of AI in life-critical settings.