Robustness
Whether a system keeps performing when inputs are noisy, unusual, or deliberately adversarial.
Robustness is performance outside the tidy test set: typos, edge cases, distribution shift, attack. A model that is accurate on clean data and collapses on real data is not accurate in any sense that matters. It is one of the trustworthiness characteristics in the NIST AI RMF and a requirement for high-risk systems in the EU.
In practice: Accuracy drops from 94% to 51% when the input photos are taken in poor light.