From Ground Truth to Measurement: A Statistical Framework for Human Labeling

TL;DR

This paper introduces a statistical framework that models human labeling as a measurement process, decomposing sources of variation to improve understanding of what models learn from noisy annotations.

Contribution

It extends classical measurement-error models to account for shared and individual notions of truth, providing a diagnostic tool for analyzing annotation sources.

Findings

Empirical evidence for all four sources of variation in multi-annotator NLP data.

Demonstrated effectiveness of the framework in decomposing labeling outcomes.

Implications for improving data quality and label interpretation in machine learning.

Abstract

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 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 better characterizes a given task. Applying the proposed model to a multi-annotator natural language inference dataset, we find empirical evidence for all four theorized components and demonstrate the effectiveness of our approach. We conclude with implications for data-centric machine learning and outline how this approach can guide the development of a more systematic science of labeling.