Mitigating Bias in Machine Learning

Brandeis Hill Marshall, Carlotta A. Berry

ISBN 10: 1264922442 / ISBN 13: 9781264922444

Editore: McGraw-Hill Education, US, 2024

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. Codice articolo LU-9781264922444

Riassunto:

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

Dati bibliografici

Titolo: Mitigating Bias in Machine Learning
Casa editrice: McGraw-Hill Education, US
Data di pubblicazione: 2024
Legatura: Paperback
Condizione: New