Abstract

Explainable Artificial Intelligence (XAI) refers to a class of computational models and methodologies designed to make the behavioral mechanisms of AI systems transparent, interpretable, and trustworthy, especially in contexts involving high‑stakes decision making. Traditional “black‑box” machine learning models such as deep neural networks and complex ensemble methods often achieve high performance yet offer limited insight into how decisions are derived. This opacity poses significant barriers to trust, accountability, and regulatory compliance in critical domains such as healthcare, finance, autonomous systems, legal sentencing, and public policy. Explainability enhances stakeholder understanding by enabling interpretation of internal model processes, decision rationales, and potential failure modes. Through a combination of model‑intrinsic explainable approaches and post‑hoc interpretation techniques, XAI fosters transparency, error diagnosis, bias detection, and ethical deployment. This paper reviews foundational and contemporary XAI methodologies up to 2021, synthesizing research on model architectures, interpretability metrics, user‑centered evaluation frameworks, and application paradigms. It proposes a methodology for assessing the effectiveness of XAI solutions in critical decision‑making systems, discusses advantages and limitations, and analyzes the role of explainability in fostering trustworthy AI adoption. The discussion highlights current challenges and outlines avenues for future research to balance performance with interpretability in AI systems deployed in real‑world contexts.