I successfully completed a project titled Explainable AI (XAI): Making AI Decisions Transparent at Yenepoya University, provided under the guidance and learning framework of IBM. This project focused on addressing one of the most critical challenges in modern Artificial Intelligence—the black-box nature of AI systems—and explored how Explainable AI helps make AI decisions more transparent, trustworthy, and accountable. As a team, we worked collaboratively to understand and present key XAI concepts such as LIME, SHAP, attention mechanisms, decision trees, and counterfactual explanations. Each member contributed actively, from research and content preparation to slide design and presentation delivery. While preparing and presenting this project, we had the opportunity to contribute ideas, clarify real-world applications, and explain complex AI concepts in a simple and meaningful way. The presentation phase was a major learning experience, allowing us to communicate technical ideas confidently and work together as a strong, coordinated team. This project strengthened my understanding of ethical and responsible AI and highlighted the importance of transparency in domains like healthcare, finance, autonomous systems, and legal decision-making. I’m grateful to Yenepoya University for providing the academic platform and to IBM for offering industry-relevant guidance. A big appreciation to my teammates for their dedication, teamwork, and shared commitment in making this project a success.