Inter-annotator Agreement

Raw matching can indicate how often annotators chose the same label, but it does not address agreement expected by chance. Cohen’s kappa adds that comparison, making it useful when researchers need more than a simple percentage. This distinction helps engineers judge whether consistency reflects meaningful labeling practices rather than coincidental matches.

Percent agreement summarizes direct matches between annotators. Cohen’s kappa evaluates agreement while accounting for agreement expected by chance, whereas Krippendorff’s alpha can accommodate multiple annotators and, in some cases, missing data. The appropriate measure therefore depends on the structure of the annotation task and the information available for comparison.

Low inter-annotator agreement may indicate that labeling criteria are ambiguous or that instructions do not guide decisions consistently. In an engineering evaluation, this signal is useful because it directs attention toward clarifying requirements, fault categories, or measurement guidance before the resulting dataset or review conclusions are treated as reproducible.

Independent annotators first assign labels, categories, or measurements to the same items. Their decisions are then compared using a suitable agreement measure, such as percent agreement, Cohen’s kappa, or Krippendorff’s alpha. Reviewing the resulting value helps determine whether the evaluation process is sufficiently consistent for its intended engineering use.

Engineering teams can apply it to requirements reviews, fault classification, image labeling, and sensor-data annotation. It also supports human judgments used to train or evaluate machine-learning systems. Across these settings, the measure provides evidence about whether different people apply the evaluation categories consistently enough to support a reproducible dataset or review.

Strong agreement supports the reproducibility of the labeled dataset or evaluation process because independent annotators reached consistent decisions on shared items. It does not replace the need to select an appropriate agreement measure, but it provides evidence that the categories and instructions were applied consistently in contexts such as requirements analysis, fault classification, or machine-learning data preparation.