Hands-on approach to ensure easy practical implementation of the concepts discussed
Most of the techniques covered are new, with only a few that refer to existing packages. For the techniques covered, the book goes deep into the subject matter and includes code to help the product teams implement these techniques for their products
Also addresses the contribution that product owners and the business analysts make to the product being fair and explainable, explaining every topic in detail, including the math involved
Covers the end-to-end view of what any software product team needs to do to be able to create a robust, successful and fair AI-driven product
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Table of contents (9 chapters)
About this book
This book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI adoption grows, it is becoming important that all AI driven products can demonstrate they are not introducing any bias to the AI-based decisions they are making, as well as reducing any pre-existing bias or discrimination.
The responsibility to ensure that the AI models are ethical and make responsible decisions does not lie with the data scientists alone. The product owners and the business analysts are as important in ensuring bias-free AI as the data scientists on the team. This book addresses the part that these roles play in building a fair, explainable and accountable model, along with ensuring model and data privacy. Each chapter covers the fundamentals for the topic and then goes deep into the subject matter – providing the details that enable the business analysts and the data scientists to implement these fundamentals.
AI research is one of the most active and growing areas of computer science and statistics. This book includes an overview of the many techniques that draw from the research or are created by combining different research outputs. Some of the techniques from relevant and popular libraries are covered, but deliberately not drawn very heavily from as they are already well documented, and new research is likely to replace some of it.
- Ethical AI
- explainable AI
- Fair Machine Learning
- Bias in AI
- Black box AI
- Data Privacy
- Ethical AI
Authors and Affiliations
Sray Agarwal, Shashin Mishra
About the authors
Book Title: Responsible AI
Book Subtitle: Implementing Ethical and Unbiased Algorithms
Authors: Sray Agarwal, Shashin Mishra
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-76977-2Published: 14 September 2021
Softcover ISBN: 978-3-030-76859-1Published: 17 September 2021
eBook ISBN: 978-3-030-76860-7Published: 13 September 2021
Edition Number: 1
Number of Pages: XIX, 177
Number of Illustrations: 11 b/w illustrations, 132 illustrations in colour
Topics: Artificial Intelligence, Machine Learning, Ethics of Technology, Computers and Society, Data Science, Computing Milieux