Flood Risk Assessment Case Study
This case study explores the development of an assurance case for an AI system that predicts flood risk to support emergency planning and property insurance decisions.
Background
Flooding causes significant economic damage and human suffering. Accurate flood risk assessment is essential for urban planning, emergency response, and insurance pricing. AI systems can integrate multiple data sources to improve prediction accuracy.
The System
The hypothetical system in this case study:
- Integrates weather forecasts, terrain data, and historical flood records
- Predicts flood probability for specific locations and timeframes
- Generates risk maps for planning and emergency response
- Provides real-time alerts during flood events
Assurance Goals
The primary assurance goal for this system might be:
“The AI-powered flood risk assessment system provides reliable and equitable predictions for use in emergency planning and insurance decisions.”
Key Considerations
Reliability
- Prediction accuracy across different flood types
- Performance under novel weather patterns
- System availability during critical periods
Equity
- Fair treatment of different communities
- Consideration of socioeconomic factors
- Accessibility of risk information
Transparency
- Communication of uncertainty in predictions
- Explainability for planning decisions
- Public access to methodology
Accountability
- Responsibility for incorrect predictions
- Update processes when predictions fail
- Stakeholder engagement in system development
Discussion Questions
- How should the system communicate uncertainty in flood predictions?
- What measures would ensure the system doesn’t disadvantage vulnerable communities?
- How would you validate the system’s predictions for rare, extreme events?
- What role should affected communities play in the assurance process?