Abstract: Pattern recognition systems — the algorithmic architectures that underpin modern machine learning — achieve their extraordinary analytical power through a mechanism that simultaneously defines their structural limitation. Trained on historical distributions, these systems identify known categories with high confidence and speed. What they cannot do is register inputs that fall outside those categories, not because of insufficient processing capacity but because of the fundamental logic of category-bound recognition itself. Adversarial examples, racial bias in facial recognition, and the catastrophic misclassification events that accompany high-stakes deployment all share a common mechanism: the outside-category input that the system does not detect as anomalous because it has no framework for anomaly outside its trained range. Properly specified human oversight is not a redundancy but a structural complement — providing the one function sophisticated pattern-recognition systems are architecturally incapable of supplying themselves: the capacity to register that an input does not fit any known category at all.
Angel Analytical Publications (2026) studied this question.