Denis Rothman - Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
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- Book:Hands-On Explainable AI (XAI) with Python: Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
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Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces.
Key Features- Learn explainable AI tools and techniques to process trustworthy AI results
- Understand how to detect, handle, and avoid common issues with AI ethics and bias
- Integrate fair AI into popular apps and reporting tools to deliver business value using Python and associated tools
Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex.
Hands-On Explainable AI (XAI) with Python will enable you to work with specific hands-on machine learning Python projects strategically arranged to enhance your grip on AI results analysis. The analysis includes building models, interpreting results with visualizations, and integrating understandable AI reporting tools and different applications.
You will build XAI solutions in Python, TensorFlow 2, Google Clouds XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source explainable AI tools for Python that can be used throughout the machine learning project life-cycle.
You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting machine learning model visualizations into user explainable interfaces.
By the end of this artificial intelligence book, you will possess an in-depth understanding of the core concepts of explainable AI.
What you will learn- Plan for explainable AI through the different stages of the machine learning life-cycle
- Estimate the strengths and weaknesses of popular open-source explainable AI applications
- Examine how to detect and handle bias issues in machine learning data
- Review ethics considerations and tools to address common problems in machine learning data
- Share explainable AI design and visualization best practices
- Integrate explainable AI results using Python models
- Use explainable AI toolkits for Python in machine learning life-cycles to solve business problems
This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book.
Some of the potential readers of this book include:
- Professionals who already use Python for as data science, machine learning, research, and analysis
- Data analysts and data scientists who want an introduction into explainable AI tools and techniques
- AI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
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Hands-On Explainable AI (XAI) with Python
Interpret, visualize, explain, and integrate reliable AI for fair, secure, and trustworthy AI apps
Denis Rothman
BIRMINGHAM - MUMBAI
Hands-On Explainable AI (XAI) with Python
Copyright 2020 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
Producer: Tushar Gupta
Acquisition Editor Peer Reviews: Divya Mudaliar
Project Editor: Tom Jacob
Content Development Editors: Kate Blackham, Alex Patterson
Copy Editor: Safis Editing
Technical Editor: Saby D'silva
Proofreader: Safis Editing
Indexer: Rekha Nair
Presentation Designer: Pranit Padwal
First published: July 2020
Production reference: 1290720
Published by Packt Publishing Ltd.
Livery Place
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Birmingham B3 2PB, UK.
ISBN 978-1-80020-813-1
www.packt.com
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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 Mot 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.
"I want to thank those corporations who trusted me from the start to deliver artificial intelligence solutions and share the risks of continuous innovation. I would also like to thank my family, who believed I would make it big at all times."
Carlos Toxtli is a human-computer interaction researcher who studies the impact of artificial intelligence in the future of work. He completed a Ph.D. in Computer Science at the University of West Virginia and a master's degree in Technological Innovation and Entrepreneurship at the Monterrey Institute of Technology and Higher Education. He has worked for numerous international organizations, including Google, Microsoft, Amazon, and the United Nations. He has also created companies that use artificial intelligence in the financial, educational, customer service, and parking industries. Carlos has published numerous research papers, manuscripts, and book chapters for different conferences and journals in his field.
"I want to thank all the editors who helped make this book a masterpiece."
In today's era of AI, accurately interpreting and communicating trustworthy AI findings is becoming a crucial skill to master. Artificial intelligence often surpasses human understanding. As such, the results of machine learning models can often prove difficult and sometimes impossible to explain. Both users and developers face challenges when asked to explain how and why an AI decision was made.
The AI designer cannot possibly design a single explainable AI solution for the hundreds of machine learning and deep learning models. Effectively translating AI insights to business stakeholders requires individual planning, design, and visualization choices. European and US law has opened the door to litigation when results cannot be explained, but developers face overwhelming amounts of data and results in real-life implementations, making it nearly impossible to find explanations without the proper tools.
In this book, you will learn about tools and techniques using Python to visualize, explain, and integrate trustworthy AI results to deliver business value, while avoiding common issues with AI bias and ethics.
Throughout the book, you will work with hands-on Python machine learning projects in Python and TensorFlow 2.x. You will learn how to use WIT, SHAP, LIME, CEM, and other key explainable AI tools. You will explore tools designed by IBM, Google, Microsoft, and other advanced AI research labs.
You will be introduced to several open source explainable AI tools for Python that can be used throughout the machine learning project lifecycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting machine learning model visualizations in user explainable interfaces.
We will build XAI solutions in Python and TensorFlow 2.x, and use Google Cloud's XAI platform and Google Colaboratory.
- Beginner Python programmers who already have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn.
- Professionals who already use Python for purposes such as data science, machine learning, research, analysis, and so on, and can benefit from learning the latest explainable AI open source toolkits and techniques.
- Data analysts and data scientists that want an introduction to explainable AI tools and techniques using Python for machine learning models.
- AI project and business managers who must face the contractual and legal obligations of AI explainability for the acceptance phase of their applications.
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