As AI transforms our world, it's crucial to ensure it is implemented responsibiy. In this session we'll explore how Azure's Machine learning service and OpenAI service, help us meet these challenges, guided by regulations like the EU AI Act, ensuring our AI systems are safe, transparent, and accountable.

Outline:

1. Brief introduction to Responsible AI principles
2. Overview of Responsible AI in Azure
Define Responsible AI in the context of Azure services
Importance of ethics and governance in AI
3. Responsible AI in Azure Machine Learning Service
Key features and tools:
Data and model transparency
Fairness and interpretability features
Case studies: How these features are applied in real-world scenarios
4. Responsible AI in Azure OpenAI Service
Integration of Responsible AI practices:
Monitoring and controlling AI models
Ensuring AI outputs align with ethical guidelines
Demonstrations of Responsible AI dashboards and scorecards
5. Practical Implementations and Case Studies
Sharing examples from recent projects
Discussion on challenges faced and how they were overcome
Insights and lessons learned from deploying Responsible AI solutions
6. Q&A and Interactive Discussion
Open the floor for audience questions
Discuss ethical dilemmas and audience experiences with AI solutions

Closing Remarks:
Recap of key points
Encourage ongoing learning and adoption of Responsible AI practices
Provide resources for further reading and exploration