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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
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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 FeaturesLearn 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 toolsBook DescriptionEffectively 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 learnPlan 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 problemsWho this book is forThis 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 analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applicationsTable of ContentsExplaining Artificial Intelligence with PythonWhite Box XAI for AI Bias and EthicsExplaining Machine Learning with FacetsMicrosoft Azure Machine Learning Model Interpretability with SHAPBuilding an Explainable AI Solution from ScratchAI Fairness with Google's What-If Tool (WIT)A Python Client for Explainable AI ChatbotsLocal Interpretable Model-Agnostic Explanations (LIME)The Counterfactual Explanations MethodContrastive XAIAnchors XAICognitive XAIFrom the Publisher
What are the key takeaways you want readers to get from this book?
In this book, you'll 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.
You'll also get to work with hands-on Python machine learning projects in Python and TensorFlow 2.x, and learn how to use WIT, SHAP, and other key explainable AI (XAI) tools - along with those designed by IBM, Google, and other advanced AI research labs.
Two of my favorite concepts that I hope readers will also fall in love with are:
The fact that XAI can pinpoint the exact feature(s) that led to an output such as SHAP, LIME, Anchors, CEM, and the other XAI methods in this bookEthics - we can finally scientifically pinpoint discrimination and eradicate it!
Finally, I would want readers to understand that it is an illusion to think that anybody can understand the output of an AI program that contains millions of parameters by just looking at the code and intermediate outputs.
What are the main tools used in the book?
The book shows you how to implement two essential tools to detect problems and bias: Facets and Google's What-If Tool (WIT). With this you'll learn to find, display, and explain bias to the developers and users of an AI project.
In addition to this, you'll use the knowledge and tools you've acquired to build an XAI solution from scratch using Python, TensorFlow, Facets, and WIT.
We often isolate ourselves from reality when experimenting with machine learning (ML) algorithms. We take the ready-to-use online datasets, use the algorithms suggested by a given cloud AI platform, and display the results as we saw in a tutorial we found on the web.
However, by only focusing on what we think is the technical aspect, we miss a lot of critical moral, ethical, legal, and advanced technical issues. In this book, we will enter the real world of AI with its long list of XAI issues, using Python as the key language to explain concepts.
Publisher : Packt Publishing (July 31, 2020)
Language : English
Paperback : 454 pages
ISBN-10 : 1800208138
ISBN-13 : 978-1800208131
Item Weight : 1.71 pounds
Dimensions : 7.5 x 1.03 x 9.25 inches
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