Model explainability refers to the ability to understand and interpret the decisions made by machine learning models. As AI systems become increasingly integrated into various sectors, the need for transparency in how these models operate has grown. Explainability allows stakeholders to grasp the “how” and “why” behind model predictions, ensuring that decisions are justifiable and trustworthy. This is crucial not only for building user confidence but also for addressing ethical concerns and regulatory requirements. Techniques such as SHAP and LIME are commonly used to provide insights into model behavior, making it easier to identify the factors influencing outcomes.