Risks of AI Risk Policy - Five Lessons

Overview

Explores challenges in implementing AI risk management frameworks, highlighting five key roadblocks faced by stakeholders when operationalizing standards like NIST AI RMF and EU AI Act.

Syllabus

• Introduction to AI Risk Management Frameworks
Overview of key AI risk management frameworks (NIST AI RMF, EU AI Act)
Importance of risk management in AI development and deployment
• Roadblock 1: Ambiguity in Standards
Challenges in interpreting broad guidelines
Case studies of misinterpretation and outcomes
Strategies for clarifying and tailoring standards to specific contexts
• Roadblock 2: Technological Complexity
Difficulty in assessing complex AI systems
Limitations of current risk assessment tools
Emerging solutions and best practices for managing complexity
• Roadblock 3: Resource Constraints
Financial and human resource limitations
Impact on small to medium-sized enterprises
Approaches to optimize resource allocation and efficiency
• Roadblock 4: Regulatory Compliance Overlap
Navigating multiple regulatory frameworks
Conflicts and redundancies in regulations
Guidance for achieving harmonized compliance
• Roadblock 5: Ethical and Social Considerations
Balancing innovation with social responsibility
Addressing bias, fairness, and transparency
Engaging stakeholders in ethical deliberation
• Conclusion and Future Directions
Summary of key roadblocks and lessons learned
Potential changes in the AI risk landscape
Encouraging proactive and adaptive risk management strategies
• Course Wrap-up and Q&A Session
Review of key concepts
Open discussion on unresolved challenges and emerging trends
Feedback and course takeaway insights

Subjects

Conference Talks