AI reliability is not a model problem. It is an evaluation problem. Most teams cannot measure whether their AI system is still correct after a model update, which means they do not know when degradation started and they cannot tell the business what their error rate actually is. The research below covers the evaluation frameworks and measurement patterns that we see in teams running AI in regulated or irreversible-decision contexts — where being wrong without knowing it is the failure mode that ends careers.
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