Foundations of Responsible AI

Learn about the practices needed to perform fairness testing and implement responsible AI systems.

Introduction

• Understanding responsible AI

1. Philosophy of AI

• What is AI and how does data enable it?
• Modern AI development
• Problems in ML that differ from software engineering

2. Data Awareness and Literacy

• Big data and where it comes from
• Seeing trends in data
• Building data understanding
• Visualization and comparing data
• Storytelling with data

3. Ethical Theories

• Introduction to ethical AI
• Ethical frameworks
• Beneficence vs. maleficence
• Calculating consequences
• Consequence scanning
• Common good and equity

4. Responsible AI Principles

• Fairness
• Transparency
• Accountability
• Explanations
• Interpretability
• Inclusivity

5. Algorithmic Harm

• Why fairness related harms?
• Critical AI incidents and learnings
• Bias in the design and development lifecycle
• Causal reasoning and fairness
• Risk mitigation in AI
• Technical aspects of sociotechnical solutions

6. Human Rights and AI

• Anonymity and data privacy
• Unintended uses and misuses
• Unethical business cases
• Autonomous systems and society
• Who AI is developed for?

Conclusion

• AI regulation and applying responsible AI frameworks