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 demand for transparency in how these models operate has grown. Explainability is crucial for building trust, ensuring accountability, and facilitating compliance with regulations. It encompasses various techniques that help stakeholders grasp the rationale behind model predictions, thereby enabling informed decision-making. By prioritizing model explainability, organizations can mitigate risks associated with deploying AI, such as unintended biases or negative social impacts, while enhancing the overall quality and reliability of their AI systems.