Beyond Inter annotator Agreement: Managing Quality with Consensus

Pairwise agreement tells you which humans make the same choices, but in order to manage data quality you need to understand where annotators converge. For high-stakes data, projects with a high-volume of annotators, or genAI evaluation use cases, consensus agreement is a must-have. But it’s not enough to know your overall agreement scores. You need to know exactly where agreement is low so you can take the right actions to improve reliability for business outcomes. In this live webinar, you’ll learn: The difference between consensus and pairwise agreement and when to use each How more granular agreement metrics help you save time and take action Why agreement calculations should be continuously integrated into quality workflows How Label Studio Enterprise enables these insights For anyone operating on the frontlines of data quality, this session will teach you how to use consensus agreement to align models with human judgment.