Annotation Noise vs. Ambiguity Distinction

A key conceptual distinction in AI data annotation: annotation noise refers to random errors caused by annotator mistakes, while meaningful ambiguity refers to the systematic phenomenon in which people from different backgrounds provide multiple reasonable interpretations of the same input.