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 LearningSocial Media and Health Information DisseminationComparative Case Study of Fairness ToolkitsBias Mitigation in Hate Speech DetectionUnintended Systematic Biases in Natural Language Processing Combating Bias in Large Language ModelsRecognizing Bias in Medical Machine Learning and AI ModelsMachine Learning Bias in HealthcareAchieving Systemic Equity in Socioecological SystemsCommunity Engagement for Machine Learning . Language: English