Session Outline
1. Why AI Governance Matters Now
- AI systems making consequential decisions
- Reputational, legal, and regulatory risks
- Stakeholder expectations: customers, employees, regulators
- The cost of getting it wrong: case examples
2. Ethical Dimensions of AI
- Fairness and bias in algorithmic systems
- Transparency and explainability requirements
- Privacy and consent in AI applications
- Accountability: who is responsible when AI errs?
3. Regulatory Landscape
- Emerging AI regulations globally
- Industry-specific requirements
- Voluntary standards and frameworks
- Preparing for regulatory change
4. Governance Frameworks in Practice
- AI ethics principles: from statements to action
- Oversight structures and committees
- Risk assessment for AI systems
- Audit and monitoring approaches
5. Board Oversight Practices
- What belongs at board level vs. management
- Information and reporting requirements
- Engaging with AI initiatives effectively
- Red flags that warrant board attention
6. Interactive Case: Governance Decision
- An AI system produces biased outcomes
- Participants act as board members
- Determine oversight actions and accountability
- Develop governance recommendations
Outcomes for Participants
- Understand the ethical risks of AI deployment
- Navigate emerging AI regulatory requirements
- Establish effective board oversight of AI
- Ask the right questions about AI governance