Description

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Learn to employ non-discriminatory, ethical, and legal machine learning procedures

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. You will explore the many different kinds of bias that occur in the field today and get mitigation strategies that are ready to deploy across a wide range of technologies, applications, and industries.

Edited by diversity experts, Eliminating Bias in Machine Learning with Practical Applications consists of chapters contributed by recognized scholars and professionals working across different artificial intelligence sectors. Each chapter addresses a different topic--including robotics, machine learning, deep learning, and natural language processing--and lays out the potentials for bias and ways of eliminating it. You will get real-world case studies throughout that highlight discriminatory machine learning practices and clearly show how they were eliminated.

• Offers cross-sector coverage that is applicable across multiple industries
• Includes online laboratory assignments, simulations, slides, video tutorials, and a code library
• Written by a team of experienced academics and industry leaders