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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: LearnQuest Network

11,552 already enrolled

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3 modules

Gain insight into a topic and learn the fundamentals.

123 reviews

Beginner level

No prior experience required

5 hours to complete

Flexible schedule

Learn at your own pace

3 modules

Gain insight into a topic and learn the fundamentals.

123 reviews

Beginner level

No prior experience required

5 hours to complete

Flexible schedule

Learn at your own pace

Skills you'll gain

Details to know

Shareable certificate

Add to your LinkedIn profile

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

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.

Module details

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

5 videosTotal 16 minutes

Course Introduction Video3 minutes
Model parity: a balancing act3 minutes
Protecting groups, protecting individuals4 minutes
Imperfect modeling5 minutes
Weekly Review1 minute

2 readingsTotal 23 minutes

The Equality Conundrum8 minutes
COMPAS article15 minutes

3 assignmentsTotal 50 minutes

Knowledge Check10 minutes
Knowledge Check10 minutes
Weekly Quiz30 minutes

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

5 videosTotal 16 minutes

Algorithms inside of algorithms: Getting to fair4 minutes
Testing in theory: fair loan decisions3 minutes
Deploying fairness: combating bias in practice3 minutes
Adversarial Models: Word2Vec4 minutes
Weekly Review: Building Fair Models1 minute

2 readingsTotal 23 minutes

Unfairness visualized 8 minutes
Research Paper: Debiasing Word Embeddings15 minutes

3 assignmentsTotal 70 minutes

Knowledge Check30 minutes
Deploying Fairness10 minutes
Exam: Building Fair Models30 minutes

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

5 videosTotal 23 minutes

Getting out of your head: bias awareness6 minutes
Building an exploratory training set6 minutes
Imperfect modeling: finding a balance5 minutes
Human Factors: Game Theory5 minutes
Weekly Review1 minute

2 readingsTotal 23 minutes

Understanding Cognitive Biases: How Mental Shortcuts Shape Our Thinking15 minutes
Game Theory and Predictive Models in Dating Apps: Insights from "Monster Match"8 minutes