Case Studies

Explore real-world examples of assurance cases across different domains. These case studies demonstrate how the TEA methodology can be applied to various AI and data-driven systems.

Available Case Studies

Case Study	Domain	Assurance Goal
Explainable Diabetic Retinopathy Screening System	Healthcare	Explainability
Fair Crop Damage Assessment System	Agriculture	Fairness
Equitable Flood Risk Assessment System	Environmental	Fairness
Explainable Student Learning Assessment System	Education	Explainability
Equitable Personalised Pharmaceutical Formulation System	Pharmaceutical	Fairness
Transparent Clinical GenAI System with Legacy Data	Healthcare	Transparency
Explainable Reinforcement Learning Agent for Air Traffic Control	Aviation	Explainability
Safe Adaptive Allocation in a Bayesian Platform Clinical Trial	Healthcare	Safety
Balancing Privacy and Utility in Census Disclosure Control	Public Sector	Privacy
Equitable Identification in Aerial Facial Recognition	Security and Defence	Fairness

How to Use These Case StudiesCopy Anchor Link

Each case study includes:

1. Overview - Background on the domain and system being assessed
2. System Description - Technical details of the AI system
3. Stakeholders - Key parties with interests in the system's assurance
4. Regulatory Context - Relevant regulations and standards
5. Assurance Considerations - Specific concerns for the assurance goal (e.g., fairness, explainability, transparency)
6. Deliberative Prompts - Questions for reflection and discussion
7. Suggested Strategies - Approaches for developing the assurance case
8. Recommended Techniques - Links to relevant TEA Techniques for gathering evidence

These case studies can be used for:

• Self-study - Work through examples at your own pace
• Workshops - Group activities and discussions
• Templates - Starting points for your own assurance cases