Artificial Intelligence Data Fairness and Bias
Learn how to identify and mitigate bias in AI systems, focusing on fairness in machine learning models and ethical data practices.
Learn how to identify and mitigate bias in AI systems, focusing on fairness in machine learning models and ethical data practices.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Ethics in the Age of AI Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
4.8
(97 ratings)
7,692 already enrolled
Instructors:
English
Powered by
What you'll learn
Understand fairness principles in machine learning
Identify and measure bias in AI systems
Implement techniques for building fair models
Analyze human factors in data collection and bias
Develop strategies for ethical AI deployment
Skills you'll gain
This course includes:
1.2 Hours PreRecorded video
9 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 3 modules in this course
This course explores the fundamental issues of fairness and bias in machine learning systems. Students learn about protecting groups and individuals in AI decision-making, building fair models, and minimizing human bias in data collection. The curriculum covers practical approaches to testing and deploying fair AI systems, from loan decisions to word embeddings, while addressing cognitive biases and ethical considerations in model development.
Fairness and protections in machine learning
Module 1 · 2 Hours to complete
Building fair models: theory and practice
Module 2 · 2 Hours to complete
Human factors: minimizing bias in data
Module 3 · 2 Hours to complete
Fee Structure
Instructor
Distinguished Technology Educator and Ethics in Computer Science Expert
Brent Summers serves as an instructor at LearnQuest, bringing expertise in software development and a unique focus on the intersection of technology and psychology. His teaching portfolio emphasizes ethical considerations in artificial intelligence and computer science. As a former education company founder, he brings entrepreneurial experience to his role as an educator. His courses focus on critical aspects of AI ethics, including algorithmic fairness, data privacy, and ethical implementation of artificial intelligence technologies. His teaching methodology combines technical expertise with psychological insights to help students understand both the technical and ethical implications of modern technology.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
4.8 course rating
97 ratings
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.