English | 2025 | Original PDF | 11 MB | 249 Pages
Carlotta A. Berry; Brandeis Hill Marshall, B0D3RXRZTS, 126492271X, 1264922442, 9781264922444, 9781264922710, 978-1264922444, 978-1264922710
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
