10:00 - 10:20am: Opening Remarks - Designing Responsible AI Infrastructure

• High Availability Systems: Strategies for creating AI systems that ensure continuous operation and minimal downtime, even under heavy workloads.
• Optimizing High-Density Workloads: Techniques for efficiently managing AI workloads to balance performance with sustainability.
• Balancing Responsibility and Performance: Discussing the trade-offs and best practices in designing AI infrastructure that meets both ethical and operational standards.

10:20 - 10:50am: Sustainable AI Computing

• Understanding the Environmental Impact: Exploring how AI model training and deployment contribute to the carbon footprint.
• Best Practices for Sustainability: Techniques to reduce energy consumption and carbon emissions in AI workflows.
• ·Real-World Examples: Case studies of AI initiatives that have successfully prioritized environmental sustainability.

10:50 - 11:20am: Compliance in AI: Navigating Global Data Privacy Laws

• Overview of Global Regulations: An in-depth look at EU GDPR, UK Data Protection Act, and US data privacy laws, focusing on their implications for AI.
• Strategies for Compliance: Ensuring AI model training processes align with data privacy regulations across different jurisdictions.
• Case Studies: Examples of AI training practices that adhere to international data privacy standards.

11:30am: Roundtable - A comprehensive discussion on the essential elements of responsible AI infrastructure.

12:15 - 12:55pm: Panel: The Future of Responsible Computing

• The Evolution of Responsible AI: Exploring how AI can evolve to be more compliant, sustainable, and resilient.
• Emerging Trends: Insights into the latest developments in AI ethics, sustainability, and regulatory compliance.
• Lessons Learned: Success stories and challenges faced by industry leaders in adopting responsible computing practices.

13:00pm: Closing Remarks