Explore a 27-minute lecture on interpretable explanations of black box models using meaningful perturbation. Delve into current challenges in interpreting black box predictors and the limitations of intuitive visualization methods. Learn about principles and methods developed to explain any black box function by determining mapping attributes and internal mechanisms. Compare various saliency methods, including gradient-based approaches and Class Activation Mapping (CAM). Understand the concept of explanations as meta-predictors and their advantages in measuring image faithfulness and finding automated explanations. Examine local explanations, meaningful image perturbations, and techniques for deletion and preservation. Discover experiments on interpretability, animal part saliency, adversarial defense, and localization. Gain insights into the development of more transparent and understandable machine learning models.