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
7,878 already enrolled
Included with
Gain insight into a topic and learn the fundamentals.
4.8
(98 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
Details to know
Earn a career certificate
Add to your LinkedIn profile
Assessments
9 assignments
Taught in English
See how employees at top companies are mastering in-demand skills
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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
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
Recommended if you're interested in Algorithms
LearnQuest
Coursera Project Network
Universidad de los Andes
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
Showing 3 of 98
4.8
98 reviews
- 5 stars
80.80%
- 4 stars
15.15%
- 3 stars
4.04%
- 2 stars
0%
- 1 star
0%
New to Algorithms? Start here.
Open new doors with Coursera Plus
Unlimited access to 7,000+ world-class courses, hands-on projects, and job-ready certificate programs - all included in your subscription
Advance your career with an online degree
Earn a degree from world-class universities - 100% online
Join over 3,400 global companies that choose Coursera for Business
Upskill your employees to excel in the digital economy
Frequently asked questions
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. 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. If you only want to read and view the course content, you can audit the course for free.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.