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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

Chapter 2, White Box XAI for AI Bias and Ethics

  1. The autopilot of an SDC can override traffic regulations. (True|False)

    True. Technically it is possible.

    False. Though possible, it is not legal.

  2. The autopilot of an SDC should always be activated. (True|False)

    False. The simulations in this chapter prove that it should be avoided in heavy traffic until autopilots can deal with any situation.

  3. The structure of a decision tree can be controlled for XAI. (True|False)

    True. Modifying the size and depth of a decision tree is a good tool to explain the algorithm.

  4. A well-trained decision tree will always produce a good result with live data. (True|False)

    True. If the training reached an accuracy of 1.

    False. New situations might confuse the algorithm.

  5. A decision tree uses a set of hardcoded rules to classify data. (True|False)

    False. A decision tree learns how to decide...

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