# DiPietro-Hazari Kappa: A Novel Metric for Assessing Labeling Quality via Annotation

arXiv: https://arxiv.org/abs/2209.08243 · HTML: https://mlsanity.com/paper/2209.08243/

## Key facts

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- First posted on arXiv 17 Sep 2022.

## Abstract (arXiv)

Data is a key component of modern machine learning, but statistics for assessing data label quality remain sparse in literature. Here, we introduce DiPietro-Hazari Kappa, a novel statistical metric for assessing the quality of suggested dataset labels in the context of human annotation. Rooted in the classical Fleiss's Kappa measure of inter-annotator agreement, the DiPietro-Hazari Kappa quantifies the the empirical annotator agreement differential that was attained above random chance. We offer a thorough theoretical examination of Fleiss's Kappa before turning to our derivation of DiPietro-Hazari Kappa. Finally, we conclude with a matrix formulation and set of procedural instructions for easy computational implementation.