AI Ethics
About the course:
You might have heard about problems that arise when AI systems misinterpret data or propose solutions that reflect human prejudice. In this course, you'll learn about the five pillars of AI ethics: fairness, robustness, explainability, transparency, and privacy. Through real-world examples you'll learn about AI ethics, how they are implemented, and why AI ethics are so important in building trustworthy AI systems.
After completing this course, you should be able to:
Identify the five pillars of AI ethics
Describe fairness in AI
Describe protected attributes
Identify privileged groups and unprivileged groups
Explain AI bias
Identify robustness
Describe adversarial robustness within AI
Explain how an adversary can influence an AI system
Identify adversarial attacks
Describe explainability
Compare interpretability and explainability
Define transparency
Describe governance
Identify the business roles and the aspects of transparency they are involved in
Identify personal information
Identify sensitive personal information
Recognize model anonymization
Describe differential privacy
Explain data minimization
