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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

Product type Paperback

Published in Jul 2020

Publisher Packt

ISBN-13 9781800208131

Length 454 pages

Edition 1st Edition

Languages

Python

Concepts

Artificial Intelligence

Author (1):

Denis Rothman

View More author details

Table of Contents (16) Chapters

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

References

The original repository for the programs that were used in this chapter can be found on GitHub: Scott Lundberg, slundberg, Microsoft Research, Seattle, WA, https://github.com/slundberg/shap

The algorithms and visualizations come from:

Su-In Lee's lab at the University of Washington
Microsoft Research

Reference for linear models: https://scikit-learn.org/stable/modules/linear_model.html

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Authors (1)

Denis Rothman

Denis Rothman

Denis Rothman graduated from Sorbonne University and Paris-Diderot University, and as a student, he wrote and registered a patent for one of the earliest word2vector embeddings and word piece tokenization solutions. He started a company focused on deploying AI and went on to author one of the first AI cognitive NLP chatbots, applied as a language teaching tool for Moët et Chandon (part of LVMH) and more. Denis rapidly became an expert in explainable AI, incorporating interpretable, acceptance-based explanation data and interfaces into solutions implemented for major corporate projects in the aerospace, apparel, and supply chain sectors. His core belief is that you only really know something once you have taught somebody how to do it.

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