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

“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 extends classical measurement-error models to accommodate both shared and individualized notions of truth, reflecting traditional and human label variation interpretations of error, and provides a diagnostic for assessing which regime”