Leonida Gianfagna (Phd, MBA) is a theoretical physicist that is currently working in Cyber Security as R&D director for Cyber Guru. Before joining Cyber Guru he worked in IBM for 15 years covering leading roles in software development in ITSM (IT Service Management). He is the author of several publications in theoretical physics and computer science and accredited as IBM Master Inventor (15+ filings).

Antonio Di Cecco is a theoretical physicist with a strong mathematical background that is fully engaged on delivering education on AIML at different levels from dummies to experts (face to face classes and remotely). The main strength of his approach is the deep-diving of the mathematical foundations of AIML models that open new angles to present the AIML knowledge and space of improvements for the existing state of art. Antonio has also a "Master in Economics" with focus innovation and teaching experiences. He is leading School of AI in Italy with chapters in Rome and Pescara

Offers a high-level perspective that explains the basics of XAI and its impacts on business and society, as well as a useful guide for machine learning practitioners to understand the current techniques to achieve explainability for AIML systems

Fills the gaps to acquire the basic knowledge both from a theoretical and a practical perspective (with examples and direct implementation) making the reader quickly capable of working with tools and code for explainable AI

Explains methods for the intrinsic interpretable ML models and agnostic methods for the non-interpretable ones

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.