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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

Summary

In this chapter, we captured the essence of XAI tools and used their concepts for cognitive XAI. Ethical and moral perspectives lead us to create a cognitive explanation method in everyday language to satisfy users who request human intervention to understand AI-made decisions.

SHAP shows the marginal contribution of features. Facets displays data points in an XAI interface. We can interact with Google's WIT, which provides counterfactual explanations, among other functions.

The CEM tool shows us the importance of the absence of a feature as well as the marginal presence of a feature. LIME takes us right to a specific prediction and interprets the vicinity of a prediction. Anchors go a step further and explain the connections between the key features of a prediction.

In this chapter, we used the concepts of these tools to help a user understand the explanations and interpretations of an XAI tool. Cognitive AI does not have the model-agnostic quality of...

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