封面
版权信息
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Contributors About the author
About the reviewer
Preface
1 Explaining Artificial Intelligence with Python
Defining explainable AI
Designing and extracting
The XAI medical diagnosis timeline
Summary
Questions
References
Further reading
2 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
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
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
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)
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
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)
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 choice of distance functions
The architecture of the deep learning model
Summary
Questions
References
Further reading
10 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 AI explanations
Anchor explanations for ImageNet
Summary
Questions
References
Further reading
12 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
Other Books You May Enjoy
Index
更新时间：2021-06-11 18:43:25
完结共113章
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