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Starts 30 June 2025 06:48

Ends 30 June 2025

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Risks of AI Risk Policy - Five Lessons

Delve into the "Risks of AI Risk Policy - Five Lessons" and explore the complexities of integrating AI risk management frameworks. This event highlights five significant challenges faced by stakeholders as they strive to apply standards like the NIST AI RMF and EU AI Act effectively. Gain insights into the vital roadblocks that can hinder the.
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Overview

Delve into the "Risks of AI Risk Policy - Five Lessons" and explore the complexities of integrating AI risk management frameworks. This event highlights five significant challenges faced by stakeholders as they strive to apply standards like the NIST AI RMF and EU AI Act effectively.

Gain insights into the vital roadblocks that can hinder the operationalization of AI protocols and learn strategies to overcome these challenges.

Whether you're an AI professional or involved in AI governance, this discussion promises to enhance your understanding of AI risk management intricacies.

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

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