
Inter-Annotator Agreement in Multi-Annotator Labeling Explained
Inter-annotator agreement is a core measure of data quality in machine learning. When multiple annotators label the same data, agreement levels reveal how consistently a task can be interpreted and how reliable the resulting labels are. Low agreement often indicates unclear guidelines, task ambiguity, or expertise mismatches rather than annotator error. Measuring and monitoring inter-annotator agreement helps teams detect label noise early, improve annotation design, and produce datasets that lead to more stable and generalizable models.
