Inter-Rater Reliability
Overview
Inter-rater reliability studies whether different annotators give consistent judgments on the same items, a key concern when labels are used to train or evaluate AI systems. Researchers use measures such as Cohen’s and Fleiss’ kappa, Krippendorff’s alpha, and the intraclass correlation coefficient, choosing among them based on the task, label scale, missing data, and class balance. Other approaches model rater-specific tendencies, aggregate multiple labels, or represent disagreement directly in settings such as essay scoring, medical image segmentation, and LLM evaluation. A central challenge is distinguishing unreliable judgment from genuine ambiguity: high agreement does not establish validity, and disagreement may reveal unclear criteria, difficult cases, or meaningful differences in interpretation.
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