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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

Introduction to SHAP

SHAP was derived from game theory. Lloyd Stowell Shapley gave his name to this game theory model in the 1950s. In game theory, each player decides to contribute to a coalition of players to produce a total value that will be superior to the sum of their individual values.

The Shapley value is the marginal contribution of a given player. The goal is to find and explain the marginal contribution of each participant in a coalition of players.

For example, each player in a football team often receives different amounts of bonuses based on each player's performance throughout a few games. The Shapley value provides a fair way to distribute a bonus to each player based on her/his contribution to the games.

In this section, we will first explore SHAP intuitively. Then, we will go through the mathematical explanation of the Shapley value. Finally, we will apply the mathematical model of the Shapley value to a sentiment analysis of movie reviews.

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