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
9,754 already enrolled
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Gain insight into a topic and learn the fundamentals.
4.7
(117 reviews)
Beginner level
No prior experience required
4 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
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Shareable certificate
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Assessments
9 assignments
Taught in English
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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 readings3 assignments
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 readings3 assignments
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 readings3 assignments
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4.5 (32 ratings)Offered by
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4.7
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Showing 3 of 117
SY
5
Reviewed on Mar 30, 2021
A relatively short and interesting course on data fairness and bias impacting AI models.
CM
4
Reviewed on Oct 2, 2021
An excellent reminder that the bias-variance trade-off is not the only trade-off machine learning specialists make.
DV
5
Reviewed on Feb 27, 2023
Really appreciate given materials, especially good reading references!
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