Model Scrutiny is a crucial part of model interpretability. It involves a detailed and rigorous evaluation of an AI model’s performance, reliability, and robustness. This includes examining the model’s predictions, testing it against different user expectations, and assessing its behavior under various conditions. Model scrutiny ensures that the model is not only interpretable but also meets the required standards and performs well in real-world scenarios. Together, these processes provide a comprehensive understanding and validation of AI models, ensuring they are both transparent and reliable.