Finding, creating, and annotating training data is one of the most intricate and painstaking tasks in machine learning (ML) model development. Many crowdsourced data annotation solutions often employ inter-annotator agreement checks to make sure their labeling team understands the labeling tasks well and is performing up to the client’s standards. However, some studies have shown that self-agreement checks are as important or even more important than inter-annotator agreement when evaluating your annotation team for quality.

In this article, we will explain what self-agreement is and introduce an ML study where self-agreement checks were crucial to the quality of the team training data and the accuracy of their model.

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