Design a comprehensive framework for interpreting black-box machine learning models. Compare feature importance methods including SHAP, LIME, and permutation importance across different model types. Address the challenges of interpreting deep neural networks, including attention mechanisms and feature visualization. Propose methods for quantifying uncertainty in model explanations. Discuss trade-offs between model performance and interpretability. Develop metrics for evaluating the quality and stability of explanations. Consider how interpretability techniques can be integrated into the model development pipeline.