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Hands-On Explainable AI (XAI) with Python

You're reading from   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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Product type Paperback
Published in Jul 2020
Publisher Packt
ISBN-13 9781800208131
Length 454 pages
Edition 1st Edition
Languages
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Author (1):
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Denis Rothman Denis Rothman
Author Profile Icon Denis Rothman
Denis Rothman
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Table of Contents (16) Chapters Close

Preface 1. Explaining Artificial Intelligence with Python 2. White Box XAI for AI Bias and Ethics FREE CHAPTER 3. Explaining Machine Learning with Facets 4. Microsoft Azure Machine Learning Model Interpretability with SHAP 5. Building an Explainable AI Solution from Scratch 6. AI Fairness with Google's What-If Tool (WIT) 7. A Python Client for Explainable AI Chatbots 8. Local Interpretable Model-Agnostic Explanations (LIME) 9. The Counterfactual Explanations Method 10. Contrastive XAI 11. Anchors XAI 12. Cognitive XAI 13. Answers to the Questions 14. Other Books You May Enjoy
15. Index

Anchors AI explanations

Anchors are high-precision model-agnostic explanations. An anchor explanation is a rule or a set of rules. The rule(s) will anchor the explanations locally. Changes to the rest of the feature values will not matter anymore for a specific instance.

The best way to understand anchors is through examples. We will define anchor rules through two examples: predicting income and classifying newsgroup discussions.

We will begin with an income prediction model.

Predicting income

In Chapter 5, Building an Explainable AI Solution from Scratch, we built a solution that could predict income levels.

We found a ground truth that has a strong influence on income: age and level of education are critical features that determine the income level of a person.

The first key feature we found was that age is a key factor when predicting the income of a person, as shown in the following chart:

Figure 11.1: Income by age

The red...

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