Designing Open AI Systems that are Trustworthy - From Models to Systems

Overview

Syllabus

• Introduction to Trustworthy AI Systems
Overview and motivation for trustworthy AI
Key challenges in AI system trustworthiness
• Balancing Autonomy and Accountability
Defining autonomy and accountability in AI
Strategies for achieving balance
Case studies of autonomous systems
• Risk Alignment in AI Systems
Understanding risks in AI deployment
Frameworks for risk assessment and management
Tools for risk mitigation
• Multi-Agent Orchestration
Principles of multi-agent systems
Coordination and communication between agents
Managing dependencies and interactions
• Governance Frameworks for AI
Regulatory requirements and compliance
Ethical considerations in AI system design
Best practices for governance and oversight
• Designing for Safe Deployment
Safety protocols and testing methodologies
Continuous monitoring and feedback loops
Examples of safe deployment in various domains
• Case Studies and Practical Applications
Real-world applications of trustworthy AI systems
Lessons learned from past implementations
Future trends and emerging technologies
• Course Summary and Future Directions
Recap of key concepts
Discussion of ongoing challenges and research areas
Opportunities for advancing trustworthy AI systems

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Subjects

Computer Science