Read online: Mitigating Bias in Machine Learning by Carlotta A. Berry, Brandeis Hill Marshall

Mitigating Bias in Machine Learning by Carlotta A. Berry, Brandeis Hill Marshall

• Mitigating Bias in Machine Learning
• Carlotta A. Berry, Brandeis Hill Marshall
• Page: 304
• Format: pdf, ePub, mobi, fb2
• ISBN: 9781264922444
• Publisher: McGraw Hill LLC

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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

Mitigating Bias in Machine Learning
1 Beyond Algorithmic Bias · 2 Going Beyond the Technical: Exploring Ethical and Societal Implications of Machine Learning · 3 Social Media and Health Information 
Mitigating bias in machine learning for medicine
Several sources of bias can affect the performance of machine learning systems used in medicine and potentially impact clinical care.
How to Reduce Bias in Machine Learning - TechTarget

What is Bias Mitigation
Bias mitigation is the process of identifying and eliminating the biases that may be present in an artificial intelligence. This may be accomplished manually or 
Bias Mitigation Strategies and Techniques for
Bias mitigation in machine learning models ; 1. Pre-processing algorithms · Relabelling and perturbation · Sampling ; 2. In-processing algorithms.
Bias Mitigation for Machine Learning Classifiers
Bias mitigation methods that outperform random baselines show that they are not only able to improve fairness but also able to perform better than naive methods 
Mitigating Bias in Machine Learning
Overview. This practical guide shows, step by step, how to use machine learning to carry out actionable decisions that do not discriminate based on numerous 
Mitigating Bias in Artificial Intelligence - Berkeley Haas
Machine learning is a common and popular subset of AI used for predictions and decision-making, but has clear limitations and issues related to bias. If you are 
Assessing and Mitigating Bias and Discrimination in AI
Course overview. This course explains what is meant by bias in the context of machine learning algorithms, how bias can be present in the training data, and how 

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