Overview
- Covers the core features of explainability and how to execute them using Python frameworks
- Explains XAI features to interpret supervised learning algorithms, NLP components and deep learning neural networks
- Covers biasness, ethics and reliability description of AI algorithms and models.
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Table of contents (14 chapters)
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Front Matter
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Back Matter
Keywords
About this book
You'll begin with an introduction to model explainability and interpretability basics, ethical consideration, and biases in predictions generated by AI models. Next, you'll look at methods and systems to interpret linear, non-linear, and time-series models used in AI. The book will also cover topics ranging from interpreting to understanding how an AI algorithm makes a decision
Further, you will learn the most complex ensemble models, explainability, and interpretability using frameworks such as Lime, SHAP, Skater, ELI5, etc. Moving forward, youwill be introduced to model explainability for unstructured data, classification problems, and natural language processing–related tasks. Additionally, the book looks at counterfactual explanations for AI models. Practical Explainable AI Using Python shines the light on deep learning models, rule-based expert systems, and computer vision tasks using various XAI frameworks.
What You'll Learn
- Review the different ways of making an AI model interpretable and explainable
- Examine the biasness and good ethical practices of AI models
- Quantify, visualize, and estimate reliability of AI models
- Design frameworks to unbox the black-box models
- Assess the fairness of AI models
- Understand the building blocks of trust in AI models
- Increase the level of AI adoption
Who This Book Is For
AI engineers, data scientists, and software developers involved in driving AI projects/ AI products.
Reviews
“While the book presents just fundamental aspects, I find this to be a great advantage. Indeed, even the layperson to AI/ML can use this work: the author starts with the most basic definitions and models, and then provides software examples … . This way a very broad readership is possible, since more advanced parts of the chapters will be interesting even for specialists in AI/ML who would like to increase their expertise in the title topic.” (Piotr Cholda, Computing Reviews, April 17, 2023)
Authors and Affiliations
About the author
Bibliographic Information
Book Title: Practical Explainable AI Using Python
Book Subtitle: Artificial Intelligence Model Explanations Using Python-based Libraries, Extensions, and Frameworks
Authors: Pradeepta Mishra
DOI: https://doi.org/10.1007/978-1-4842-7158-2
Publisher: Apress Berkeley, CA
eBook Packages: Professional and Applied Computing, Apress Access Books, Professional and Applied Computing (R0)
Copyright Information: Pradeepta Mishra 2022
Softcover ISBN: 978-1-4842-7157-5Published: 15 December 2021
eBook ISBN: 978-1-4842-7158-2Published: 14 December 2021
Edition Number: 1
Number of Pages: XVIII, 344
Number of Illustrations: 50 b/w illustrations, 144 illustrations in colour
Topics: Artificial Intelligence, Python