In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.
This course is part of the Ethics in the Age of AI Specialization
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About this Course
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Try Coursera for BusinessSkills you will gain
- machine learning fairness
- Ethics
- data bias
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Syllabus - What you will learn from this course
Fairness and protections in machine learning
Building fair models: theory and practice
Human factors: minimizing bias in data
Reviews
- 5 stars83.54%
- 4 stars13.92%
- 3 stars2.53%
TOP REVIEWS FROM ARTIFICIAL INTELLIGENCE DATA FAIRNESS AND BIAS
Really appreciate given materials, especially good reading references!
Extraodinary course! I've learnt so much! The classes are very informative and dynamic. Didn't feel like studying but rather entertaining myself with hight quality content! Thank you so much!
An excellent reminder that the bias-variance trade-off is not the only trade-off machine learning specialists make.
A relatively short and interesting course on data fairness and bias impacting AI models.
About the Ethics in the Age of AI Specialization

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