Artificial Intelligence (AI) has emerged as a transformative technology influencing decision-making processes across diverse sectors, including healthcare, finance, education, employment, law enforcement, and public administration. While AI systems offer significant advantages in terms of efficiency, accuracy, and scalability, many advanced models operate as “black boxes,” producing outputs without providing clear explanations of how decisions are reached. This lack of transparency raises serious ethical, legal, and social concerns, particularly when AI-driven decisions affect fundamental rights, opportunities, and public trust. This paper explores the concept of moving beyond the black box by examining the interconnected principles of transparency, explainability, and accountability in Artificial Intelligence. Transparency refers to the disclosure of information regarding the design, functioning, and data sources of AI systems, while explainability focuses on making AI decisions understandable to users, stakeholders, and regulators. Accountability ensures that individuals, organizations, and developers remain responsible for the outcomes generated by AI technologies. The study analyzes the challenges associated with opaque algorithms, including bias, discrimination, privacy violations, and the difficulty of assigning responsibility for harmful outcomes. Further, the paper reviews emerging regulatory approaches, ethical guidelines, and governance frameworks designed to promote responsible AI development and deployment. It highlights the importance of human oversight, explainable AI techniques, risk assessment mechanisms, and organizational accountability structures in fostering trust and fairness. By integrating ethical principles with technological innovation, the research argues that transparency and explainability are essential prerequisites for meaningful accountability in AI systems. The paper concludes that as AI becomes increasingly embedded in societal decision-making processes, establishing robust mechanisms for transparency, explainability, and accountability is critical to ensuring that technological advancement aligns with democratic values, human rights, and principles of justice. Such measures will be instrumental in building public confidence and promoting the responsible use of Artificial Intelligence in the future.