This chapter explores transparency as a foundational principle in AI ethics and governance. It begins by defining transparency and related concepts such as explainability, interpretability, accountability, and bias mitigation. The chapter introduces the three core dimensions of transparency—process, decision, and data—and explains their significance in building ethical and trustworthy AI systems. It also addresses the challenges of black-box AI models and proposes technical and governance-based solutions like Explainable AI (XAI), model documentation, and transparency-by-design. A range of case studies (e.g., IBM Watson, ZestFinance, YouTube) and global regulatory frameworks (EU AI Act, TAIBOM, US AI Bill of Rights) highlight real-world applications, illustrating how transparency enables public trust, compliance, fairness, and ethical accountability in AI.