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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Ethics in the Age of AI SpecializationLearnQuestAbout this Course
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Course 2 of 4 in the
Beginner Level
Approx. 6 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Skills you will gain
machine learning fairnessEthicsdata bias
Flexible deadlines
Reset deadlines in accordance to your schedule.
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 2 of 4 in the
Beginner Level
Approx. 6 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Offered by
LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
Syllabus - What you will learn from this course
2 hours to complete
Fairness and protections in machine learning
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
2 hours to complete
5 videos (Total 19 min), 2 readings, 3 quizzes
2 hours to complete
Building fair models: theory and practice
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?
2 hours to complete
5 videos (Total 21 min), 2 readings, 3 quizzes
2 hours to complete
Human factors: minimizing bias in data
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
2 hours to complete
5 videos (Total 33 min), 2 readings, 3 quizzes
Reviews
TOP REVIEWS FROM ARTIFICIAL INTELLIGENCE DATA FAIRNESS AND BIAS
by SYMar 30, 2021
A relatively short and interesting course on data fairness and bias impacting AI models.
About the Ethics in the Age of AI Specialization
As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations. In this specialization, we will explore the rise of algorithms, fundamental issues of fairness and bias in machine learning, and basic concepts involved in security and privacy of machine learning projects. We'll finish with a study of 3 projects that will allow you to put your new skills into action.
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