CONCEPT
Interpretation Error
A new category of AI-era error — the person's prompt is clear and the system's execution is correct, but the system's interpretation of the prompt diverges from the person's meaning, producing an output that is technically right and practically wrong.
Norman's classical error taxonomy distinguished slips (right plan, wrong execution) from mistakes (wrong plan, correct execution). Both categories assumed deterministic systems where error originated in the user. The AI era introduces errors that originate neither in the user nor in the system but in the semantic space between them — what Chapter 4 of the Norman volume calls interpretation errors. The user asks for an authentication system; she gets one that uses password-based authentication when she intended OAuth. The word was interpreted in a particular way the user did not specify because she assumed the specification was unnecessary — because in a conversation with a human colleague, shared context would have resolved the ambiguity without explicit negotiation.
In The You On AI Field Guide
Interpretation errors are insidious because they are invisible on the surface. The output compiles. The tests pass. The artifact exists. The error is semantic — a gap between what was