While machine learning models excel in predictive accuracy, traditional evaluation metrics often overlook critical issues like bias, fairness, and transparency. This talk emphasizes the urgent need for Responsible AI to build trustworthy ML systems. We'll explore common ML challenges, discuss the limitations of traditional evaluation methods, and advocate for incorporating Responsible AI metrics to ensure model robustness. To address the black-box nature of many models, we'll underscore the importance of explainability and provide a live demonstration of Explainable AI techniques to shed light on model decision-making processes.