Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Introduction to Responsible AI
What you will learn in this course
What you will learn in this course (1:52)
Foundations of Responsible AI
What is Responsible AI, and why does responsibility matter? (19:48)
Ethical principles: fairness, accountability, transparency, and ethics (FATE). (11:49)
Key challenges in AI ethics: bias, privacy, and explainability. (19:16)
Quiz: Foundations of Responsible AI
Bias and the Social Implications of AI
Types of bias in AI: data, algorithmic, and user-induced. (5:49)
Impact of AI in Society (14:41)
Methods for detecting and mitigating bias. (3:13)
Legal implications of bias in AI. (15:48)
Quiz: Bias and the Social Implications of AI
Transparency and Explainability
Why transparency is crucial for trust in AI. (2:03)
Explainability vs. interpretability: What’s the difference? (9:10)
Quiz: Transparency and Explainability
AI Governance and Compliance
Overview of AI governance frameworks. (3:33)
Key regulations: EU AI Act, Colorado AI Act, and local policies. (6:28)
Role of audits and certifications in responsible AI. (2:32)
Quiz: AI Governance and Compliance
The Future of Responsible AI
Building a career in responsible AI. (26:26)
Types of bias in AI: data, algorithmic, and user-induced.
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock