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