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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. Mitigating Bias in Machine Learning addresses:
Ethical and Societal Implications of Machine Learning
Social Media and Health Information Dissemination
Comparative Case Study of Fairness Toolkits
Bias Mitigation in Hate Speech Detection
Unintended Systematic Biases in Natural Language Processing
Combating Bias in Large Language Models
Recognizing Bias in Medical Machine Learning and AI Models
Machine Learning Bias in Healthcare
Achieving Systemic Equity in Socioecological Systems
Community Engagement for Machine Learning
This textbook is ideal for undergraduate or graduate students or those seeking an introduction to ML. Since there are few textbooks with practical applications of ML, this contribution will fill in the gap by introducing the topic with an emphasis on a real-world perspective and implementations