Practical Fairness: Achieving Fair And Secure Data Models

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Synopsis
Fairness is an increasingly important topic as machine learning and AI more generally take over the world. While this is an active area of research, many realistic best practices are emerging at all steps along the data pipeline, from data selection and preprocessing to blackbox model audits. This book will guide you through the technical, legal, and ethical aspects of making your code fair and secure while highlighting cutting edge academic research and ongoing legal developments related to fairness and algorithms.There is mounting evidence that the widespread deployment of machine learning and artificial intelligence in business and government is reproducing the same biases we are trying to fight in the real world. For this reason, fairness is an increasingly important consideration for the data scientist. Yet discussions of what fairness means in terms of actual code are few and far between. This code will show you how to code fairly as well as cover basic concerns related to data security and privacy from a fairness perspective.
Copyright:
2021

Book Details

Book Quality:
Publisher Quality
Book Size:
326 Pages
ISBN-13:
9781492075684
Related ISBNs:
9781492075691, 9781492075707, 9781492075738
Publisher:
O'Reilly Media
Date of Addition:
Copyrighted By:
Aileen Nielsen
Adult content:
No
Language:
English
Has Image Descriptions:
No
Categories:
Nonfiction, Computers and Internet, Business and Finance, Law, Legal Issues and Ethics, Sociology
Submitted By:
Bookshare Staff
Usage Restrictions:
This is a copyrighted book.

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