This chapter explores the critical importance of explainability in artificial intelligence systems, particularly in high-stakes domains such as healthcare, finance, criminal justice, and autonomous decision-making. As AI increasingly influences significant aspects of human life, the ability to understand and interpret AI-generated decisions has become an ethical imperative rather than merely a technical consideration. The chapter defines explainability in AI as the extent to which humans can comprehend how an AI system arrives at decisions, encompassing both technical aspects like model interpretability and ethical dimensions including individuals’ rights to explanations for automated decisions affecting them. The chapter then delves into technical approaches to achieving explainability, including interpretable models like decision trees and linear regression, as well as post-hoc explanation techniques such as SHAP and LIME. The chapter examines various conceptual frameworks for understanding explainability, including intrinsic versus post-hoc explainability, local versus global explainability, human-centred approaches, and regulatory dimensions. It analyses different technical approaches to achieving explainability, from inherently interpretable models (like decision trees and linear regression) to post-hoc explanation techniques such as SHAP and LIME. Through case studies including Amazon’s recruitment AI and healthcare prediction failures, the chapter illustrates the serious ethical consequences of inadequate explainability. The chapter concludes by proposing a comprehensive framework for embedding explainability throughout the AI development lifecycle—from requirements analysis to deployment and monitoring—and outlining best practices for ensuring AI systems operate transparently, fairly, and in alignment with human values. A best practices framework provides practical guidance for developers, emphasizing the importance of early consideration of explainability requirements, documentation of model decisions, and continuous monitoring of explanation effectiveness. The chapter concludes by critically evaluating current approaches, identifying challenges like the trade-off between model accuracy and interpretability, and suggesting future directions for the field.