After successful completion of the course, students are able to...
understand the concepts, ethics, governance, methods, tools, and regulation behind responsible AI; as well as to have the basic knowledge on bias and fairness, interpretability and explainability, and algorithmic audits. Finally, they will understand how to design responsible algorithmic systems.
Content
1. Introduction (2 hours)
Why responsible AI?
Examples of current issues
2. Human Intelligence (2 hours)
The brain
Consciousness and intelligence
3. Artificial Intelligence (2 hours)
Overview
Limitations of data
Limitations of methods
Evaluation problems
4. AI Ethics (2 hours)
Values
Instrumental principles
Ethical risk assessments
5. AI Governance (2 hours)
People and processes
Tools
6. Bias and Fairness (2 hours)
Definitions
Measures
Challenges
7. Interpretable & Explainable AI (2 hours)
Interpretability
Explainability
Challenges
8. Algorithmic Audits (2 hours)
Main concepts
Detailed example
9. Data & AI Regulation (2 hours)
Data privacy
AI Act (EU)
AI Bill of Rights (US)
10. Design of Responsible AI Systems (2 hours)
Human-centered AI
AI for Good
