Crop Damage Assessment Case Study
This case study examines the development of an assurance case for an AI system that uses satellite imagery to assess crop damage for insurance purposes.
Background
Agricultural insurance is crucial for farmers’ livelihoods and food security. Traditional crop damage assessment is time-consuming and expensive, requiring physical inspections. AI systems using satellite and drone imagery can provide faster, more consistent assessments.
The System
The hypothetical system in this case study:
- Analyses satellite and aerial imagery of agricultural land
- Detects and quantifies crop damage from various causes
- Generates reports for insurance claim processing
- Provides historical comparison and trend analysis
Assurance Goals
The primary assurance goal for this system might be:
“The AI-powered crop damage assessment system provides fair and accurate assessments for agricultural insurance claims.”
Key Considerations
Accuracy
- Detection sensitivity across different crop types
- Performance in varying weather and lighting conditions
- Calibration against ground-truth assessments
Fairness
- Consistent treatment across different farm sizes
- Performance across different geographical regions
- Consideration of smallholder farmers’ needs
Transparency
- Explainability of damage assessments
- Clear communication of system limitations
- Audit trail for disputed claims
Reliability
- System availability during critical assessment periods
- Data quality and imagery resolution requirements
- Handling of edge cases and unusual situations
Discussion Questions
- How would you ensure the system treats all farmers fairly, regardless of farm size or location?
- What evidence would demonstrate the system’s accuracy compared to human assessors?
- How should disputes about AI-generated assessments be handled?
- What stakeholders beyond farmers and insurers should be considered?