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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

Interpretability and explainability from an ethical AI perspective

It comes as no surprise that Google's WIT AI fairness example notebook comes with a biased dataset. It is up to us to use WIT to point out the unethical or moral dilemmas contained in the COMPAS dataset.

COMPAS stands for Correctional Offender Management Profiling for Alternative Sanctions. Judges and parole officers are said to score the probability of recidivism for a given defendant.

In this section, our task will be to transform the dataset into an unbiased, ethical dataset. We will analyze COMPAS with people-centered AI before importing it.

We will first describe the ethical and legal perspectives. Then we will define AI explainability and interpretability for our COMPAS example. Finally, we will prepare an ethical dataset for our model by improving the feature names of the dataset.

Our ethical process fits the spirit of Google's research team when they designed WIT for us to...

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