Data Science Ethics (Oxford University Press)
Friday, 15 July 2022

This book looks at data science ethics - all about what is right and wrong when conducting data science. David Martens looks at the ethical considerations that come from data science, and highlights different concepts and techniques that aid understanding, ranging from k-anonymity and differential privacy to homomorphic encryption and zero-knowledge proofs to address privacy concerns, techniques to remove discrimination against sensitive groups, and various explainable AI techniques.

<ASIN:0192847279>

Real-life cautionary tales further illustrate the importance and potential impact of data science ethics, including tales of racist bots, search censoring, government backdoors, and face recognition. The book is punctuated with structured exercises that provide hypothetical scenarios and ethical dilemmas for reflection that teach readers how to balance the ethical concerns and the utility of data.

Author: David Martens
Publisher: Oxford University Press
Date: June 2022
Pages: 272
ISBN: 978-0192847270
Print:0192847279
Audience: General
Level: Intermediate
Category: Data Science

dataethic

For recommendations of Big Data books see Reading Your Way Into Big Data in our Programmer's Bookshelf section.

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Street Coder (Manning)

Author: Sedat Kapanoglu
Publisher: Manning
Date: February 2022
Pages: 272
ISBN: 978-1617298370
Print: 1617298379
Kindle: B09Q3PJQC5
Audience: General
Rating: 4
Reviewer: Ian Elliot
Street Coder - sounds sort of tough but messy at the same time.



Machine Learning Q and AI (No Starch Press)

Author: Sebastian Raschka
Publisher: No Starch Press
Date: April 2024
Pages: 264
ISBN: 978-1718503762
Print: 1718503768
Kindle: B0CKKXCK3T
Audience: Developers interested in AI
Rating: 4
Reviewer: Mike James
Q and AI, a play on Q&A is a clever title, but is the book equally clever?


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