Book Image

Hands-On Explainable AI (XAI) with Python

By : Denis Rothman
Book Image

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.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
1
Explaining Artificial Intelligence with Python
Explaining Artificial Intelligence with Python
Defining explainable AI
Designing and extracting
The XAI medical diagnosis timeline
Summary
Questions
References
Further reading
Free Chapter
2
White Box XAI for AI Bias and Ethics
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
3
Explaining Machine Learning with Facets
Explaining Machine Learning with Facets
Getting started with Facets
Facets Overview
Sorting the Facets statistics overview
Facets Dive
Summary
Questions
References
Further reading
4
Microsoft Azure Machine Learning Model Interpretability with SHAP
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
5
Building an Explainable AI Solution from Scratch
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
6
AI Fairness with Google's What-If Tool (WIT)
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
7
A Python Client for Explainable AI Chatbots
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
8
Local Interpretable Model-Agnostic Explanations (LIME)
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
The LIME explainer
Summary
Questions
References
Further reading
9
The Counterfactual Explanations Method
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
10
Contrastive XAI
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
Pertinent negatives
Summary
Questions
References
Further reading
11
Anchors XAI
Anchors XAI
Anchors AI explanations
Anchor explanations for ImageNet
Summary
Questions
References
Further reading
12
Cognitive XAI
Cognitive XAI
Cognitive rule-based explanations
A cognitive approach to vectorizers
Human cognitive input for the CEM
Summary
Questions
Further reading
13
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
14
Other Books You May Enjoy
Other Books You May Enjoy
15
Index
Index

Summary

In this chapter, we explored a powerful XAI tool. We saw how to analyze the features of our training and testing datasets before running an ML model.

We saw that Facets Overview could detect features that bring the accuracy of our model down because of missing data and too many records containing zeros. You can then correct the datasets and rerun Facets Overview.

In this iterative process, Facets Overview might confirm that you have no missing data but that the data distributions of one or more features have high levels of non-uniformity. You might want to go back and investigate the values of these features in your datasets. You can then either improve them or replace them with more stable features.

Once again, you can rerun Facets Overview and check the distribution distance between your training and testing datasets. If the Kullback-Leibler divergence is too significant, for example, you know that your ML model will produce many errors.

...
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End of Section 6
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