SECAI Core · phase 6 of 15
Model testing
A process for ensuring the most appropriate model is used for each task, the program handles the input correctly, and the output is in line with what was expected
The Explain card
- Plain English
- Model testing checks that the most appropriate model is used for each task, that the program handles inputs correctly, and that outputs match what was expected.
- Example
- A team routes short classification jobs to a small model and complex reasoning to a larger one. Testing confirms each route picks the right model, that malformed or oversized inputs are rejected cleanly, and that outputs conform to the expected schema.
- Why it matters
- Testing catches integration failures that evaluation metrics miss: the wrong model answering, inputs that crash the pipeline, outputs that break downstream parsing. Defenders add abuse cases so failure modes are found before attackers find them.
- Hook
- Model testing asks three things: right model, input handled, output expected.
Where it sits in the deck
Phase 6: Model Quality, Ethics, and Responsible AI Principles
Before deploying a model, understand the quality and ethical dimensions — bias, fairness, explainability, and the human oversight needed to govern outputs responsibly.
