Hands-On Explainable AI (XAI) with Python

By : Denis Rothman

Hands-On Explainable AI (XAI) with Python

By: Denis Rothman

Overview of this book

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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Get in touch

Explaining Artificial Intelligence with Python

Defining explainable AI

Designing and extracting

The XAI medical diagnosis timeline

Summary

Questions

References

Further reading

Free Chapter

White Box XAI for AI Bias and Ethics

Moral AI bias in self-driving cars

Standard explanation of autopilot decision trees

XAI applied to an autopilot decision tree

Using XAI and ethics to control a decision tree

Summary

Questions

References

Further reading

Explaining Machine Learning with Facets

Getting started with Facets

Facets Overview

Sorting the Facets statistics overview

Summary

Microsoft Azure Machine Learning Model Interpretability with SHAP

Introduction to SHAP

Getting started with SHAP

Linear models and logistic regression

Summary

Questions

References

Further reading

Building an Explainable AI Solution from Scratch

Moral, ethical, and legal perspectives

The U.S. census data problem

The machine learning perspective

WIT applied to a transformed dataset

Summary

Questions

References

Further reading

AI Fairness with Google's What-If Tool (WIT)

Interpretability and explainability from an ethical AI perspective

Getting started with WIT

Creating a DNN model

Creating a SHAP explainer

Model outputs and SHAP values

The WIT datapoint explorer and editor

Summary

Questions

References

Further reading

A Python Client for Explainable AI Chatbots

The Python client for Dialogflow

Enhancing the Google Dialogflow Python client

A CUI XAI dialog using Google Dialogflow

Summary

Questions

Further reading

Local Interpretable Model-Agnostic Explanations (LIME)

Introducing LIME

Getting started with LIME

An experimental AutoML module

Interpreting the scores

Training the model and making predictions

Summary

The Counterfactual Explanations Method

The counterfactual explanations method

The choice of distance functions

The architecture of the deep learning model

Summary

Questions

References

Further reading

Contrastive XAI

The contrastive explanations method

Getting started with the CEM applied to MNIST

Defining and training the CNN model

Defining and training the autoencoder

Summary

Anchors XAI

Anchors AI explanations

Anchor explanations for ImageNet

Summary

Questions

References

Further reading

Cognitive XAI

Cognitive rule-based explanations

A cognitive approach to vectorizers

Human cognitive input for the CEM

Summary

Questions

Further reading

Answers to the Questions

Chapter 1, Explaining Artificial Intelligence with Python

Chapter 2, White Box XAI for AI Bias and Ethics

Chapter 3, Explaining Machine Learning with Facets

Chapter 4, Microsoft Azure Machine Learning Model Interpretability with SHAP

Chapter 5, Building an Explainable AI Solution from Scratch

Chapter 6, AI Fairness with Google's What-If Tool (WIT)

Chapter 7, A Python Client for Explainable AI Chatbots

Chapter 8, Local Interpretable Model-Agnostic Explanations (LIME)

Chapter 9, The Counterfactual Explanations Method

Chapter 10, Contrastive XAI

Chapter 11, Anchors XAI

Chapter 12, Cognitive XAI

Other Books You May Enjoy

Index

Customer Reviews

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Getting started with the CEM applied to MNIST

In this section, we will install the modules and the dataset. The program will also prepare the data for the CNN model.

Open the CEM.ipynb program that we will use in this chapter.

We will begin by installing and importing the modules we need.

Installing Alibi and importing the modules

We will first install Alibi by trying to import Alibi:

# @title Install Alibi
try:
  import alibi
except:
  !pip install alibi

If Alibi is installed, the program will continue. However, when Colaboratory restarts, some libraries and variables are logs. In this case, the program will install Alibi.

We will now install the modules for the program.

Importing the modules and the dataset

In this section, we will import the necessary modules for this program. We will then import the data and display a sample.

Open the CEM.ipynb program that we will use in this chapter.

We will first import the modules.

Hands-On Explainable AI (XAI) with Python

By : Denis Rothman

Hands-On Explainable AI (XAI) with Python

By: Denis Rothman

Overview of this book

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