Explainable AI with Python [electronic resource] /

1 -- The Landscape -- 1.1 Examples of what Explainable AI is -- 1.1.1 Learning Phase -- 1.1.2 Knowledge Discovery -- 1.1.3 Reliability and Robustness -- 1.1.4 What have we learnt from the 3 examples -- 1.2 Machine Learning and XAI -- 1.2.1 Machine Learning tassonomy -- 1.2.2 Common Myths -- 1.3 The need for Explainable AI -- 1.4 Explainability and Interpretability: different words to say the same thing or not? -- 1.4.1 From World to Humans -- 1.4.2 Correlation is not causation -- 1.4.3 So what is the difference between interpretability and explainability? -- 1.5 Making Machine Learning systems explainable -- 1.5.1 The XAI flow -- 1.5.2 The big picture -- 1.6 Do we really need to make Machine Learning Models explainable? -- 1.7 Summary -- 1.8 References -- 2. Explainable AI: needs, opportunities and challenges -- 2.1 Human in the loop -- 2.1.1 Centaur XAI systems -- 2.1.2 XAI evaluation from “Human in The Loop perspective” -- 2.2 How to make Machine Learning models explainable -- 2.2.1 Intrinsic Explanations -- 2.2.2 Post-Hoc Explanations -- 2.2.3 Global or Local Explainability -- 2.3 Properties of Explanations -- 2.4 Summary -- 2.5 References -- 3 Intrinsic Explainable Models -- 3.1.Loss Function -- 3.2.Linear Regression -- 3.3.Logistic Regression -- 3.4.Decision Trees -- 3.5.K-Nearest Neighbors (KNN) -- 3.6.Summary -- 3.7 References -- 4. Model-agnostic methods for XAI -- 4.1 Global Explanations: permutation Importance and Partial Dependence Plot -- 4.1.1 Ranking features by Permutation Importance -- 4.1.2 Permutation Importance on the train set -- 4.1.3 Partial Dependence Plot -- 4.1.4 Properties of Explanations -- 4.2 Local Explanations: XAI with Shapley Additive explanations -- 4.2.1 Shapley Values: a game-theoretical approach -- 4.2.2 The first use of SHAP -- 4.2.3 Properties of Explanations -- 4.3 The road to KernelSHAP -- 4.3.1 The Shapley formula -- 4.3.2 How to calculate Shapley values -- 4.3.3 Local Linear Surrogate Models (LIME) -- 4.3.4 KernelSHAP is a unique form of LIME -- 4.4 Kernel SHAP and interactions -- 4.4.1 The NewYork Cab scenario -- 4.4.2 Train the Model with preliminary analysis -- 4.4.3 Making the model explainable with KernelShap -- 4.4.4 Interactions of features -- 4.5 A faster SHAP for boosted trees -- 4.5.1 Using TreeShap -- 4.5.2 Providing explanations -- 4.6 A naïve criticism to SHAP -- 4.7 Summary -- 4.8 References -- 5. Explaining Deep Learning Models -- 5.1 Agnostic Approach -- 5.1.1 Adversarial Features -- 5.1.2 Augmentations -- 5.1.3 Occlusions as augmentations -- 5.1.4 Occlusions as an Agnostic XAI Method -- 5.2 Neural Networks -- 5.2.1 The neural network structure -- 5.2.2 Why the neural network is Deep? (vs shallow) -- 5.2.3 Rectified activations (and Batch Normalization) -- 5.2.4 Saliency Maps -- 5.3 Opening Deep Networks -- 5.3.1 Different layer explanation -- 5.3.2 CAM (Class Activation Maps) and Grad-CAM -- 5.3.3 DeepShap / DeepLift -- 5.4 A critic of Saliency Methods -- 5.4.1 What the network sees -- 5.4.2 Explainability batch normalizing layer by layer -- 5.5 Unsupervised Methods -- 5.5.1 Unsupervised Dimensional Reduction -- 5.5.2 Dimensional reduction of convolutional filters -- 5.5.3 Activation Atlases: How to tell a wok from a pan -- 5.6 Summary -- 5.7 References -- 6.Making science with Machine Learning and XAI -- 6.1 Scientific method in the age of data -- 6.2 Ladder of Causation -- 6.3 Discovering physics concepts with ML and XAI -- 6.3.1 The magic of autoencoders -- 6.3.2 Discover the physics of damped pendulum with ML and XAI -- 6.3.3 Climbing the ladder of causation -- 6.4 Science in the age of ML and XAI -- 6.5 Summary -- 6.6 References -- 7. Adversarial Machine Learning and Explainability -- 7.1 Adversarial Examples (AE) crash course -- 7.1.2 Hands-on Adversarial Examples -- 7.2 Doing XAI with Adversarial Examples -- 7.3 Defending against Adversarial Attacks with XAI -- 7.4 Summary -- 7.5 References -- 8. A proposal for a sustainable model of Explainable AI -- 8.1 The XAI "fil rouge" -- 8.2 XAI and GDPR -- 8.2.1 FAST XAI -- 8.3 Conclusions -- 8.4 Summary -- 8.5 References -- Index.

This book provides a full presentation of the current concepts and available techniques to make “machine learning” systems more explainable. The approaches presented can be applied to almost all the current “machine learning” models: linear and logistic regression, deep learning neural networks, natural language processing and image recognition, among the others. Progress in Machine Learning is increasing the use of artificial agents to perform critical tasks previously handled by humans (healthcare, legal and finance, among others). While the principles that guide the design of these agents are understood, most of the current deep-learning models are "opaque" to human understanding. Explainable AI with Python fills the current gap in literature on this emerging topic by taking both a theoretical and a practical perspective, making the reader quickly capable of working with tools and code for Explainable AI.