1. Ethical Theories
Introduction to ethical AI
()
Beneficence vs. maleficence
()
Operationalizing ethics
()
2. Responsible AI Principles
Fairness and inclusivity
()
Transparency and accountability
()
Explainability and interpretability
()
3. Algorithmic Harm
Critical AI incidents and learnings
()
Bias in the design and development lifecycle
()
Risk mitigation in AI
()
Autonomous systems and society
()
4. Human Rights and AI
Anonymity and data privacy
()
Unintended uses and misuses
()
Socio-technical change
()
Who AI is developed for
()
Conclusion
What you can do
()