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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

The choice of distance functions

WIT uses a distance function for its visual representation of data points.

If you click on the information symbol (i) next to Nearest counterfactual, WIT shows the three distance function options available:

Figure 9.19: Choosing a distance function

In this section, we will go through these options, starting with the L1 norm:

  • The L1 norm
  • The L2 norm
  • Custom distance

The L1 norm

The L1 norm is the Manhattan or taxicab norm because it calculates distances in a way that resembles the blocks in Manhattan. The following diagram shows a grid like the blocks of buildings in Manhattan, New York City:

Figure 9.20: Manhattan distance

Suppose you take a cab at point [0, 0] and want to go to point [5, 6]. The driver will go 5 blocks in a straight line (see the blocks in gray) and will then turn to the left and drive 6 blocks (see the path in gray). You will pay a distance of 5 + 6 blocks = 11 ...

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