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⇱ Artificial Intelligence Data Fairness and Bias | Coursera

Artificial Intelligence Data Fairness and Bias

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Artificial Intelligence Data Fairness and Bias

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Gain insight into a topic and learn the fundamentals.
4.8

123 reviews

Beginner level
No prior experience required
5 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
4.8

123 reviews

Beginner level
No prior experience required
5 hours to complete
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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

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.

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 videos•Total 16 minutes
  • Course Introduction Video•3 minutes
  • Model parity: a balancing act•3 minutes
  • Protecting groups, protecting individuals•4 minutes
  • Imperfect modeling•5 minutes
  • Weekly Review•1 minute
2 readings•Total 23 minutes
  • The Equality Conundrum•8 minutes
  • COMPAS article•15 minutes
3 assignments•Total 50 minutes
  • Weekly Quiz•30 minutes
  • Knowledge Check•10 minutes
  • Knowledge Check•10 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 videos•Total 16 minutes
  • Algorithms inside of algorithms: Getting to fair•4 minutes
  • Testing in theory: fair loan decisions•3 minutes
  • Deploying fairness: combating bias in practice•3 minutes
  • Adversarial Models: Word2Vec•4 minutes
  • Weekly Review: Building Fair Models•1 minute
2 readings•Total 23 minutes
  • Unfairness visualized •8 minutes
  • Research Paper: Debiasing Word Embeddings•15 minutes
3 assignments•Total 70 minutes
  • Exam: Building Fair Models•30 minutes
  • Knowledge Check•30 minutes
  • Deploying Fairness•10 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 videos•Total 23 minutes
  • Getting out of your head: bias awareness•6 minutes
  • Building an exploratory training set•6 minutes
  • Imperfect modeling: finding a balance•5 minutes
  • Human Factors: Game Theory•5 minutes
  • Weekly Review•1 minute
2 readings•Total 23 minutes
  • Understanding Cognitive Biases: How Mental Shortcuts Shape Our Thinking•15 minutes
  • Game Theory and Predictive Models in Dating Apps: Insights from "Monster Match"•8 minutes
3 assignments•Total 42 minutes
  • Weekly Quiz•30 minutes
  • Human Bias•6 minutes
  • Models under the influence•6 minutes

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Instructor

Instructor ratings
4.5 (35 ratings)
LearnQuest
207 Courses•1,000,012 learners

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Showing 3 of 123

RU
·

Reviewed on Apr 19, 2022

Really great discussion of algorithms and how their designs make them susceptible to bias.

NN
·

Reviewed on Apr 30, 2026

Thanks for lectures , and help me have a choice for choose this major

SY
·

Reviewed on Mar 30, 2021

A relatively short and interesting course on data fairness and bias impacting AI models.

Frequently asked questions

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

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