Explainable AI with Python

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.

Includes index.

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…