The lecture is part II of the cycle Cybersecurity I-IV (data protection, explainability, robustness/attacks, certification) and includes:

1. Introduction, Motivation, Definitions
   Taxonomy and assumptions, brief overview of ideas from different categories, representation of explanation results, saliency maps, feature importance, first applications
2. Black-box (model-agnostic) explanations
   Additive feature attribution method and  properties, LIME and other variants, SHAP, Shapley values, from local explanation to global understanding Implementation details regarding neighborhood construction (on-manifold explanations), risks of random perturbations
3. White-box (model-specific) explanations
   LRP, DTD, DeepLIFT, Grad-CAM, Counterfactual explanations
4. Information-theoretic explanation methods
   Information decomposition, causality, theory of representation learning
5. Application and Implementation
   Debugging, model extraction, challenges, trade-off (e.g., explainability vs. privacy)