The document discusses explainable AI (XAI) and making machine learning and deep learning models more interpretable. It covers the necessity and principles of XAI, popular model-agnostic XAI methods for ML and DL models, frameworks like LIME, SHAP, ELI5 and SKATER, and research questions around evolving XAI to be understandable by non-experts. The key topics covered are model-agnostic XAI, surrogate models, influence methods, visualizations and evaluating descriptive accuracy of explanations.
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