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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

Cognitive rule-based explanations

Machine intelligence can produce formidable algorithms and explainable AI tools.

However, it takes the self-awareness of human consciousness to understand.

Cognitive XAI adds a layer of understanding for a user to reduce the need for human intervention.

In Chapter 7, A Python Client for Explainable AI Chatbots, we designed the first steps of an XAI chatbot. Cognitive XAI will bring explainable AI to chatbots in the future to reduce the need for human resources and hotlines.

Cognitive XAI will by no means replace machine intelligence XAI. Cognitive XAI is not yet model-agnostic. You can imagine as many approaches as necessary for each project.

The goal in this section and chapter is to think like a human when explaining AI to a user who needs to understand a system in order to trust it.

The first step is to go from XAI tools to XAI concepts.

From XAI tools to XAI concepts

In this section, we will list...

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