Artificial Intelligence Data Fairness and Bias
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.
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Syllabus
Syllabus - What you will learn from this course
Week 1
Fairness and protections in machine learning
Week 2
Building fair models: theory and practice
Week 3
Human factors: minimizing bias in data
FAQ
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Reviews
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!
A relatively short and interesting course on data fairness and bias impacting AI models.
An excellent reminder that the bias-variance trade-off is not the only trade-off machine learning specialists make.
Really great discussion of algorithms and how their designs make them susceptible to bias.
