Practical Fairness: Achieving Fair and Secure Data Models

In today's data-driven world, organizations are increasingly relying on machine learning (ML) and data analytics to make critical decisions. However, these technologies can perpetuate existing biases and create new ones, leading to unfair and discriminatory outcomes. Practical Fairness addresses this challenge by providing a comprehensive guide to building fair and secure data models.

Practical Fairness: Achieving Fair and Secure Data Models

by Aileen Nielsen

4.8 out of 5

Language	:	English
File size	:	7598 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	347 pages
Screen Reader	:	Supported
Paperback	:	104 pages
Reading age	:	9 - 12 years
Grade level	:	4 - 6
Item Weight	:	4 ounces
Dimensions	:	5 x 0.24 x 8 inches

FREE

What is Practical Fairness?

Practical Fairness is a book written by Jenny Bryan and Kate Crawford, two leading researchers in the field of fairness in machine learning. The book provides a practical approach to mitigating bias and ensuring data privacy in machine learning and data analytics. It covers a wide range of topics, including:

• Understanding the different types of bias
• Developing techniques for mitigating bias
• Ensuring data privacy
• Evaluating the fairness and security of data models

Why is Practical Fairness Important?

Practical Fairness is important because it provides a practical guide to building fair and secure data models. This is essential for organizations that want to use machine learning and data analytics to make fair and ethical decisions. The book also raises important questions about the social and ethical implications of these technologies.

Who Should Read Practical Fairness?

Practical Fairness is a must-read for anyone who is interested in building fair and secure data models. This includes data scientists, machine learning engineers, data analysts, and anyone else who works with data. The book is also a valuable resource for policymakers and regulators who are interested in understanding the challenges and opportunities of fair and secure data models.

How to Get Practical Fairness

Practical Fairness is available in paperback, hardcover, and ebook formats. You can Free Download the book from Our Book Library, Barnes & Noble, or your favorite bookstore.

Practical Fairness is an essential guide to building fair and secure data models. This book provides a practical approach to mitigating bias and ensuring data privacy in machine learning and data analytics. It is a must-read for anyone who is interested in these technologies.

Practical Fairness: Achieving Fair and Secure Data Models

by Aileen Nielsen

4.8 out of 5

Language	:	English
File size	:	7598 KB
Text-to-Speech	:	Enabled
Enhanced typesetting	:	Enabled
Word Wise	:	Enabled
Print length	:	347 pages
Screen Reader	:	Supported
Paperback	:	104 pages
Reading age	:	9 - 12 years
Grade level	:	4 - 6
Item Weight	:	4 ounces
Dimensions	:	5 x 0.24 x 8 inches

FREE