Summary: Policies, bias considerations, security, data privacy, and responsible use frameworks. How to adopt AI without creating new risks.

Outcome: A comprehensive AI governance framework

Takeaway: Practical policies and controls for responsible AI adoption

Key Discussion Points:

• AI governance framework components and ownership
• Bias detection and mitigation strategies
• Data privacy considerations in AI training and inference
• Security controls for AI systems and models
• Regulatory landscape and compliance requirements
• Vendor AI vs. internal AI governance differences
• Transparency and explainability requirements
• Human oversight and escalation procedures
• Risk assessment methodology for AI use cases
• Monitoring and ongoing governance processes