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.
Artificial Intelligence Data Fairness and Bias
Artificial Intelligence Data Fairness and Bias
This course is part of Ethics in the Age of AI Specialization
Instructor: Brent Summers
4,412 already enrolled
Course
Gain insight into a topic and learn the fundamentals
4.8
(80 reviews)
Beginner level
No previous experience necessary
6 hours (approximately)
Flexible schedule
Learn at your own pace
Skills you'll gain
- Category: machine learning fairness
- Category: Ethics
- Category: data bias
Details to know
Shareable Certificate
Add to your LinkedIn profile
Quizzes and assessments
6 quizzes, 3 assessments
English
Subtitles: French, Portuguese (European),
Course
Gain insight into a topic and learn the fundamentals
4.8
(80 reviews)
Beginner level
No previous experience necessary
6 hours (approximately)
Flexible schedule
Learn at your own pace
Build your subject-matter expertise
This course is part of the Ethics in the Age of AI Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course
Welcome to the course! In week one, we will be discussing what fairness means in the context of machine learning and what true parity means in different scenarios
What's included
5 videos2 readings2 quizzes
This week we will take action against unfairness. Now that we have an understanding of fairness issues, how do we build models that do not violate them?
What's included
5 videos2 readings2 quizzes
This week, we will tackle the human biases that enter the data collection and attribute selection processes. The goal? Removing bias before the model is built
What's included
5 videos2 readings2 quizzes
Instructor
Instructor ratings
4.6 (25 ratings)Offered by
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Frequently asked questions
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:
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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.
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