Mitigating bias in machine learning / edited by Carlotta A. Berry, Brandeis Hill Marshall.

Format:

Book

Contributor:

Berry, Carlotta A., editor.

Marshall, Brandeis (Brandeis Hill), editor.

Series:

McGraw-Hill's AccessEngineeringLibrary

Language:

English

Subjects (All):

Machine learning--Moral and ethical aspects.

Machine learning.

Genre:

Textbooks.

Physical Description:

1 online resource (259 pages) : illustrations

Edition:

First edition.

Place of Publication:

New York : McGraw-Hill Education, [2025]

Language Note:

In English.

Summary:

This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous human factors, including ethnicity and gender. The authors examine the many kinds of bias that occur in the field today and provide mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries. Edited by engineering and computing experts, Mitigating Bias in Machine Learning includes contributions from recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic and real-world case studies are featured throughout that highlight discriminatory machine learning practices and clearly show how they were reduced. - back cover

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

ISBN:

9781264922710

126492271X

OCLC:

1455762404