HIRAF case studies
HIRAF · Put your knowledge to work in context.
HIRAF is a professional certification exam focused on the governance and responsible deployment of artificial intelligence. It is aimed at practitioners who need to reason about AI systems beyond model accuracy: risk analysts, compliance and audit staff, data and ML engineers moving into governance roles, product managers overseeing AI features, and consultants advising organisations on AI controls. The HIRAF case studies are 5 cases and about 2,927 words.
1The Stalled Triage Model After a Vendor Dataset Refresh
A hospital's radiology AI team deployed a chest X-ray triage model nine months ago. Internal validation showed AUROC 0.93 and sensitivity 0.91 at the chosen threshold.
586 words, about 3 min2The Recommendation Engine and the Inference Log
A retail platform runs a product recommendation model trained on 14 months of clickstream data. The privacy team learns that the feature pipeline stores per-user embedding vectors in a logging bucket for 30 days to support debugging.
583 words, about 3 min3The Fraud Model That Flags One Neighborhood
A bank deploys a fraud-detection model that scores transactions in real time. The model uses 40 features, including merchant category, transaction amount, time of day, and a 'distance from home address' feature.
604 words, about 3 min4The Model Card That Says 'High Accuracy'
A public-sector agency procures a document-classification system to route benefit applications. The vendor's model card reports 96% accuracy on a benchmark of 50,000 documents and lists intended use as 'administrative document triage.' The agency's own pilot on 3,000 real applications finds 91% accuracy overall, but 74% on handwritten forms, which are common among applicants over 65 and applicants in rural areas.
575 words, about 3 min5The Drift Alert Nobody Owned
A logistics company runs a demand-forecasting model that drives warehouse staffing. It was trained on 2021-2022 data and has run unchanged for 18 months.
579 words, about 3 min