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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

Questions

  1. The autopilot of an SDC can override traffic regulations. (True|False)
  2. The autopilot of an SDC should always be activated. (True|False)
  3. The structure of a decision tree can be controlled for XAI. (True|False)
  4. A well-trained decision tree will always produce a good result with live data. (True|False)
  5. A decision tree uses a set of hardcoded rules to classify data. (True|False)
  6. A binary decision tree can classify more than two classes. (True|False)
  7. The graph of a decision tree can be controlled to help explain the algorithm. (True|False)
  8. The trolley problem is an optimizing algorithm for trollies. (True|False)
  9. A machine should not be allowed to decide whether to kill somebody or not. (True|False)
  10. An autopilot should not be activated in heavy traffic until it's totally reliable. (True|False)
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Hands-On Explainable AI (XAI) with Python
Published in: Jul 2020
Publisher: Packt
ISBN-13: 9781800208131
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