From Ground Truth to Measurement: A Statistical Framework for Human Labeling
Supervised machine learning assumes that labeled data provide accurate measurements of the concepts models are meant to learn. Yet in practice, human labeling introduces systematic variation arising from ambiguous items, divergent interpretations, and simple mistakes. Machine learning research commonly treats all disagreement as noise, which obscures these distinctions and limits our understanding of what models actually learn. This paper reframes annotation as a measurement process and introduces a statistical framework for decomposing labeling outcomes into interpretable sources of variation: instance difficulty, annotator bias, situational noise, and relational alignment. The framework...
| Authors | Robert Chew, Stephanie Eckman, Christoph Kern et al. |
|---|---|
| Primary category | stat.ME |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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