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Starts 6 June 2025 09:33
Ends 6 June 2025
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46 minutes
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Overview
Explore theoretical foundations of Trustworthy AI with Sanjit Seshia, examining key concepts and principles that underpin reliable artificial intelligence systems.
Syllabus
- Introduction to Trustworthy AI
- Theoretical Foundations
- Verification and Validation of AI Systems
- Fairness in AI
- Transparency and Explainability
- Security and Privacy in AI
- Accountability and Governance
- Case Studies and Applications
- Future Directions in Trustworthy AI
- Conclusion and Reflection
Definition and Importance of Trustworthy AI
Key Challenges in Building Trustworthy AI Systems
Formal Methods in AI
Computational Models of Trust
Reliability and Robustness in AI
Formal Verification Techniques
Testing and Debugging AI Models
Model Checking and Theorem Proving
Definitions of Fairness
Bias Detection and Mitigation
Ethical and Social Implications
Interpretability of Machine Learning Models
Techniques for Model Explanation
Human-AI Interaction and Trust
Adversarial Attacks and Defenses
Data Privacy Techniques
Secure AI Model Deployment
Responsibility in AI Systems
Policy and Regulatory Frameworks
Standards for Trustworthy AI
Real-world Examples of Trustworthy AI
Lessons Learned from Successful Implementations
Emerging Trends and Technologies
Research Challenges and Opportunities
Summary of Key Concepts
Pathways for Further Study in Trustworthy AI
Subjects
Computer Science
