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
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Artificial Intelligence Data Fairness and Bias
This course is part of Ethics in the Age of AI Specialization
Instructor: Brent Summers
8,049 already enrolled
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4.8
(102 reviews)
Beginner level
No prior experience required
6 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Skills you'll gain
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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
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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 assignments2 discussion prompts
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 assignments1 discussion prompt
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 assignments1 discussion prompt
Instructor
Instructor ratings
4.6 (28 ratings)Offered by
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Fred Hutchinson Cancer Center
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