Textbook: Mitigating Bias in Machine Learning

Although Machine Learning (ML) has recently experienced exponential growth, there are few texts about the presence of bias or discussions of promoting ethics in robotics, artificial intelligence, and machine learning. The authors and editors propose publication of text from the perspective of diverse communities (women, Black, Latino, Hispanic, Indigenous, Disabled, LGBTQ+, International including Africa, Central and South America).

In addition, there are few textbooks with practical applications of machine learning for undergraduate and graduate students. In order to enhance ML courses for these students, there is a need for a textbook with laboratory assignments, simulations, etc., so students can appreciate the richness of the field through active learning. This book will serve as an introductory text on ML that surveys several tools and illustrates how to use various tools for collecting and managing data and implementing practical algorithms, in addition to addressing bias and promoting ethics.

Call for Chapters (McGraw-Hill)

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We invite contributors to submit chapters to the Machine Learning book tentatively titled “Mitigating Bias in Machine Learning”. We invite chapters relevant to our goals to enhance students’ active learning / experiences, mitigate bias, promote ethics and amplify perspectives about these topics. We invite authors from diverse communities.

MLTextbook Call for Chapter Form

Support or Contact

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If you have any questions, please email editor at machinelearningtext@gmail.com.

Editor:

Carlotta A. Berry

Professor

Lawrence J. Giacoletto Endowed Chair in Eletrical Engineering

ROSE-HULMAN INSTITUTE OF TECHNOLOGY