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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. Understanding the theory of an ML algorithm is enough for XAI. (True|False)
  2. Explaining the origin of datasets is not necessary for XAI. (True|False)
  3. Explaining the results of an ML algorithm is sufficient. (True|False)
  4. It is not necessary for an end user to know what a KNN is. (True|False)
  5. Obtaining data to train an ML algorithm is easy with all the available data online. (True|False)
  6. Location history is not necessary for a medical diagnosis. (True|False)
  7. Our analysis of the patient with the West Nile virus cannot be applied to other viruses. (True|False)
  8. A doctor does not require AI to make a diagnosis. (True|False)
  9. It isn't necessary to explain AI to a doctor. (True|False)
  10. AI and XAI will save lives. (True|False)
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