This chapter provides an in-depth exploration of the fundamental ethical principles governing AI development and deployment. The chapter begins by establishing the context of AI’s growing influence across various sectors and the corresponding need for ethical frameworks. It focuses on four core principles: transparency, accountability, fairness, and non-maleficence. The chapter examines each principle in detail, providing frameworks and workflows for their implementation. Transparency is explored through a process model that ensures AI systems’ decisions are explainable and understandable. Accountability is addressed through a framework that assigns clear responsibilities and establishes oversight mechanisms. The fairness principle is examined through a bias detection workflow that helps identify and mitigate discriminatory outcomes. Finally, non-maleficence is discussed through a framework focused on preventing harm and managing risks. Real-world examples and case studies, particularly in facial recognition systems, demonstrate the practical application and importance of these principles. The chapter emphasizes the interconnected nature of these principles and their crucial role in building trustworthy AI systems.