Theoretical Aspects of Trustworthy AI - Introduction

Overview

Join Sanjit Seshia in an insightful exploration of Trustworthy AI's theoretical foundations. This introduction addresses the key concepts and principles vital to developing reliable artificial intelligence systems.

Perfect for those interested in understanding the intricacies of AI trustworthiness, this session offers invaluable insights into ensuring AI systems' integrity and reliability.

Offered through YouTube, this course falls under the categories of Artificial Intelligence Courses and Computer Science Courses, making it a crucial addition to your learning journey in the realm of AI.

Syllabus

• Introduction to Trustworthy AI
Definition and Importance of Trustworthy AI
Key Challenges in Building Trustworthy AI Systems
• Theoretical Foundations
Formal Methods in AI
Computational Models of Trust
Reliability and Robustness in AI
• Verification and Validation of AI Systems
Formal Verification Techniques
Testing and Debugging AI Models
Model Checking and Theorem Proving
• Fairness in AI
Definitions of Fairness
Bias Detection and Mitigation
Ethical and Social Implications
• Transparency and Explainability
Interpretability of Machine Learning Models
Techniques for Model Explanation
Human-AI Interaction and Trust
• Security and Privacy in AI
Adversarial Attacks and Defenses
Data Privacy Techniques
Secure AI Model Deployment
• Accountability and Governance
Responsibility in AI Systems
Policy and Regulatory Frameworks
Standards for Trustworthy AI
• Case Studies and Applications
Real-world Examples of Trustworthy AI
Lessons Learned from Successful Implementations
• Future Directions in Trustworthy AI
Emerging Trends and Technologies
Research Challenges and Opportunities
• Conclusion and Reflection
Summary of Key Concepts
Pathways for Further Study in Trustworthy AI

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Subjects

Computer Science