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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

Designing and extracting

Extracting data from the outputs of an AI model is one way of providing XAI data. Another approach consists of designing outputs at each phase of an AI solution from the start. Explainable components can be designed for the inputs, the model, the outputs, the events occurring when the AI model is in production, and the accountability requirements.

We need an XAI executive function to visualize how explainable models fit into each phase of an AI process.

The XAI executive function

Our executive function in everyday life includes our way of thinking and managing our activities. We can follow directions and focus on certain things, for example, using our executive function.

Our brain uses an executive function to control our cognitive processes. AI project managers use executive functions to monitor all of the phases of an AI system.

A representation of XAI through an executive function will help you make your way through the many ways to...

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