Mitigating Bias in Machine Learning

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