"Interpretability and explainability are key considerations beyond predictive accuracy for building trust in machine learning systems for high-stakes applications. There are many different ways of explaining that are relevant for different use cases and personas who consume the explanations. Denis Rothman has done a good job in providing step-by-step tutorial examples in Python to provide an entrée into this important topic, focusing on one of the ways of explaining: post hoc local explanations."

Kush R. Varshney, Distinguished Research Staff Member and Manager, Foundations of Trustworthy AI, IBM Research

"Hands-On Explainable AI (XAI) with Python is a timely book on a complex subject, and it fulfills its promise. The book covers the whole spectrum i.e. XAI for types of users, XAI for phases of a project, legal issues, data issues etc. It also covers techniques like LIME, SHAP from Microsoft, and WIT from Google, and also explores implementation scenarios like healthcare, self-driving cars etc. There is a lot to learn from this book both in the breadth and depth and it is a recommended read."

Ajit Jaokar, Principal Data Scientist/AI Designer, Feynlabs.ai, and Director, FutureText

"The timing of Denis Rothman's book Hands-on Explainable AI (XAI) with Python is perfect. Not only does the book provide a solid overview of the XAI concepts and challenges necessitated by XAI, but it is a perfect catalyst for those data scientists who want to get their hands dirty exploring different XAI techniques."

--

Bill Schmarzo, Dean of Big Data, Author of The Economics of Data, Analytics, and Digital Transformation

"Hands-on Explainable AI (XAI) with Python covers XAI white box models for the explainability and interpretability of algorithms with transparency for the accuracy of predictable outcomes and results from XAI applications keeping ethics in mind. Denis Rothman shows how to install LIME, SHAP, and WIT tools and the ethical standards to maintain balanced datasets with best practices and principles. The book is a recommended read for data scientists."

Dr. Ganapathi Pulipaka, Chief Data Scientist, Chief AI HPC Scientist, DeepSingularity

Biografía del autor Denis Rothman graduated from Sorbonne University and Paris-Diderot University, writing one of the very first word2vector embedding solutions. He began his career authoring one of the first AI cognitive natural language processing (NLP) chatbots applied as a language teacher for Moët et Chandon and other companies. He has also authored an AI resource optimizer for IBM and apparel producers. He then authored an advanced planning and scheduling (APS) solution that is used worldwide. Denis is an expert in explainable AI (XAI), having added interpretable mandatory, acceptance-based explanation data and explanation interfaces to the solutions implemented for major corporate aerospace, apparel, and supply chain projects.