From Ground Truth to Measurement: A Statistical Framework for Human Labeling
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Le résumé fourni par la source
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.
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Où se fait cette recherche
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LMU Klinikum pays non établi dans la noticeÉtablissement de santé
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Munich Center for Machine Learning pays non établi dans la noticeUniversité ou école supérieure
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Ludwig-Maximilians-Universität München pays non établi dans la noticeUniversité ou école supérieure
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University of Maryland Department of AI pays non établi dans la noticeUniversité ou école supérieure
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Department of Statistics pays non établi dans la noticeInstitution
LMU Klinikum, Munich Center for Machine Learning et Ludwig-Maximilians-Universität München, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.