Explainable AI: Making Machine Learning Decisions Transparent And Interpretable — A Review Of Methods, Evaluation And Human-Centred Explanation

Machine learning models increasingly influence decisions in healthcare, finance, hiring and public services, yet many of the most accurate models are complex black boxes whose reasoning cannot be inspected. Explainable Artificial Intelligence (XAI) aims to make these decisions transparent and interpretable so that users can understand, trust, debug and contest them. This paper reviews XAI as a general discipline rather than for a single domain. It clarifies concepts of interpretability, explainability, transparency and faithfulness, and organizes methods into inherently interpretable models, global explanations such as partial dependence and accumulated local effects, local feature attribution with LIME, SHAP and integrated gradients, visual explanations for deep networks, concept-based explanations and counterfactuals. Evaluation of explanations, including sanity checks that reveal misleading saliency maps, and insights from social science on what makes a good explanation are discussed. A five-layer explainable ML pipeline comprising data, model, explanation engine, evaluation and audience-specific presentation is proposed, with research gaps, objectives, a comparison of methods and challenges such as the accuracy–interpretability trade-off, unstable explanations and regulation. The review concludes that explanations should be faithful, evaluated and designed for the people who use them.