Synopsis
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
"synopsis" may belong to another edition of this title.
About the Author
Carlotta A. Berry is a professor in the Department of Electrical and Computer Engineering at Rose-Hulman Institute of Technology, where she is also Dr. Lawrence J. Giacoletto Endowed Chair.
Brandeis Hill Marshall is founder and CEO of DataedX Group, a data ethics learning and development agency. She is a thought leader in broadening participating in data science and puts inclusivity and equity at the center of her work. She obtained her doctorate in Computer Science from Rensselaer Polytechnic Institute.
"About this title" may belong to another edition of this title.
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Paperback. Condition: new. Paperback. 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 Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781264922444
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Paperback. Condition: New. 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. Seller Inventory # LU-9781264922444
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