Explainable AI (XAI) for trustworthy and transparent decision-making: A theoretical framework for AI interpretability

Explainable Artificial Intelligence (XAI) has become a critical area of research in addressing the black-box nature of complex AI models, particularly as these systems increasingly influence high-stakes domains such as healthcare, finance, and autonomous systems. This study presents a theoretical framework for AI interpretability, offering a structured approach to understanding, implementing, and evaluating explainability in AI-driven decision-making. By analyzing key XAI techniques, including LIME, SHAP, and DeepLIFT, the research categorizes explanation methods based on scope, timing, and dependency on model architecture, providing a novel taxonomy for understanding their applicability across different use…