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
Book Description
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 see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications.
You will build XAI solutions in Python, TensorFlow 2, Google Cloud's 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 XAI 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 the visualization of machine learning models into user explainable interfaces.
By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI.
What you will learn
- Plan for XAI through the different stages of the machine learning life cycle
- Estimate the strengths and weaknesses of popular open-source XAI 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 XAI design and visualization best practices
- Integrate explainable AI results using Python models
- Use XAI toolkits for Python in machine learning life cycles to solve business problems
Who this book is for
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
Table of Contents
- Explaining Artificial Intelligence with Python
- White Box XAI for AI Bias and Ethics
- Explaining Machine Learning with Facets
- Microsoft Azure Machine Learning Model Interpretability with SHAP
- Building an Explainable AI Solution from Scratch
- AI Fairness with Google's What-If Tool (WIT)
- A Python Client for Explainable AI Chatbots
- Local Interpretable Model-Agnostic Explanations (LIME)
- The Counterfactual Explanations Method
- Contrastive XAI
- Anchors XAI
- Cognitive XAI
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.
