Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps

About This BookThis is a book I've been waiting for for a few years. Explainable AI (XAI) is the next step in artificial intelligence theory and practice. In this book, Denis Rothman explores the currently available technologies of XAI and discusses the theory behind it as well as the legal hurdles they will help us cross. The technologies range from straight Python to various offerings from Microsoft and Google.Who is This For?The preface gives a wide range of potential readers, but I think the author pulls this off. You can easily read the theory without getting bogged down by the code, or you could work through the examples and have a good basic knowledge to apply the theory to your next project.Why Was This Written?Explainable AI is still a very new subfield of AI and there are very few texts written about it. Rothman came through at just the right time with this book. AI cannot progress much further if it continues to be a black box.OrganizationThere is no overall organization to this book, but this is a fairly new field, so that's understandable. There is a nice flow that makes sure that a new topic is introduced cleanly before being used to extend another technique.The microstructure is well suited for this type of book. Each chapter has a summary, questions, references, and further reading. Given the amount of theory in this book, the questions (largely true or false) are a useful aid in recall. The further reading section is very welcome to extend the reader's knowledge even deeper.Did This Book Succeed?I can easily say that, yes, the author reached his stated goals. This is a book that any serious AI theorist or practitioner should have in their library. Any student of AI should read through this book and practice the exercises to be relevant in the field. I hope to add a physical copy of this book to my library in the near future.Rating and Final ThoughtsThis is the book the Denis Rothman needed to write. I was very critical about his last book, but knew that he had a lot of knowledge and understanding to contribute to the field. I am very pleased to say that this is it. Rothman pushes our understanding of AI forward, in more ways than one.I am happy to give this book a 5 out of 5 and look forward to Rothman's next book.

I have definitely heard grumblings about the "black box" nature of ML models, but I am new to reading specifically about XAI. I am curious about: what exactly does XAI mean? And, what are people doing? This book provides answers to both of these questions, in a non-academic, example and code based way. Right now, this is the only book of its kind, with a focus on Python.XAI is about creating a "glass box", transparent model, aka a white box model. By doing so, we gain "information on the inner workings of the algorithms". We can use this information to explain and interpret model results.The book starts off with a medical diagnosis problem, based on the K-Nearest Neighbors algorithm. It continues with methods and tools for "explaining", such as Facets, SHAP, Google's What-If-Tool (WIT), LIME, Counterfactual Explanations, Contrastive XAI, Anchors XAI, and Cognitive XAI.At the end of each chapter there are three very nice features:1) Questions - To help check one's understanding of the material, with the answers at the end of the book2) References - links to github repositories with relevant code, and/or documentation3) Further Reading - links to papers or websites for more information about the methodsI read that the literature on XAI is increasing very quickly, with everyone offering up a new approach. This book brings a lot of topics together in one place, and could give a person a hands-on, quick start to the field.

Denis Rothman assembles tools that open the curtain of the Wizard of Oz (AI). Hands-on Explainable AI (XAI) with Python is one book you need that introduces libraries and methods for turning Blackbox AI into Whitebox.Rotham aims to help ML developers and stakeholders build AI applications that are interpretable, reliable, fair and trustworthy. He covered SHAPE, LIME, and Alibi, as well as Facet and Google’s What-If Tool, as tools for model interpretability and visualization.What stands out most out of Rothman’s contribution is both explanations on of storytelling coverage of current regulations such as GDPR, and hands-on codes in Jupyter Notebooks to show how to implement Python’s Data Science libraries to address such concerns.

As many who work or just do projects about machine learning or data science, there are many libraries/Algorithms which are very easy to use. You put your data in one side and you receive the results from the other. We all understand those results and maybe a few easier supervised learning models, but there comes a point where not knowing exactly what is going on in these black boxes can make it hard to explain to interested individuals and even harder to convince say your boss, on a particular model to implement where money and time is valuable. This book goes into further detail with examples and storylines which are extremely lacking in other machine learning literature out there. The examples in this book are closer to real world applications and some examples have been utilized in the real world. This book on its own does a very well job in what it does and the explanations given are clearly from someone with academic and "REAL WORLD" experience as many times examples given in other literature are more academic in nature and difficult to make a connection on where to utilize in a project.

If you're looking to buy this book, then I don't need to tell you about the explosion in ML and AI-related applications across many industries over the last decade: e-commerce, streaming services, automobiles, finance, imaging, virtual assistants, etc.Yet, for such a burgeoning field there is a dearth of resources when it comes to explainable AI. Part of the problem is that Machine Learning tools have become so refined and user-friendly that it is no longer necessary to have a good understanding of the core principles before calling an API and making predictions; just about any non-technical person could be trained to carry out a few easy steps to get low-hanging results. Thus, the demand is pretty high for introductory texts that walk the users through the many techniques for handling data, training models, and presenting predictions.However, when it comes to unearthing the insights that lead to such predictions, the literature is sadly lacking. It used to be the case that if you needed to train an easily explainable model, you could get away with some sort of regression or decision-tree based approach. But the quantity and complexity of data in recent times have led us into the territory of "black-box models": Neural Networks and Gradient-boosted trees. While these are very powerful with very intricate architecture that can handle everything from images, text, video, sound, to even creative processes, they are not easily explainable.More and more countries are recognizing the uses and misuses of AI tools and calling for legislation to reign in the scope and manner in which these tools are applied and rightfully questioning the process by which they were designed and if their creators have taken full account of the possible consequences. Just in the USA, SR11-7 has been written with an eye to curb model risk. In the EU, you have to look no further than the GDPR (“General Data Protection Regulation”). Among their chief concerns is the issue of built-in bias.So, the days are coming to an end when you could easily build models and deploy them and hide behind their accuracy as long as they got you the results you wanted.That's why I think this book is a bit of a gem: it's getting the ball rolling in training ML practitioners in not only recognizing the need to explain AI models, but more importantly giving them the tools to do so.I wish I had a book like this two, or even one year ago when I was developing explainers for Anomaly Detection and Neural networks for the financial sector.It is both highly accessible and authoritative in its survey of methods for extracting root-cause level intelligence of the prediction effected by the models. It is very current as well: SHAP, LIME, Google's WhatIf, and more are discussed with several illuminating examples to help the reader grasp the concepts and practice.What is even better, is that the author does not hide behind the same datasets used over-and-over again in every ML text, such as MNIST, CIFAR, Boston Housing, Titanic, etc.. This alone, is so refreshing and it made it such a please to read. I wish more would follow his lead.Overall, I whole-heartedly recommend this book to any ML&AI practitioner looking to understand their models and data better, and more importantly, to those looking to future-proof their organization's AI capital.