Programme outline
Learning objectives
By the end of the course, participants will be able to:
- Understand core principles of artificial intelligence (AI) quality management
- Identify risks and challenges in AI development and deployment
- Evaluate AI systems using quality metrics and assessment tools
- Integrate risk management into AI workflows
- Communicate AI quality considerations to stakeholders
Day 1
- Introduction to Artificial Intelligence
- Risks and Challenges of Artificial Intelligence
- Issues with Artificial Intelligence Model Quality
- Artificial Intelligence Data Quality Issues
- Artificial Intelligence Governance
- Introduction to ISO SC42
- Interpretation of the “EU AI ACT”
Day 2
- Artificial Intelligence Quality Management Framework
- Artificial Intelligence Model Quality Management
- Artificial Intelligence Data Quality Management
- Artificial Intelligence Computing System Quality Management
- Artificial Intelligence Application Scenarios and Quality Issues
- Case study, including group discussion
- Personal action plan
Assessment
- Case study-based group exercise, group presentation
- Hands-on risk and compliance mapping activity, group discussion and presentation