CLARA: Confidence of Labels and Raters

Large online services employ thousands of people to label content for applications such as video understanding, natural language processing, and content policy enforcement. While labelers typically reach their decisions by following a well-defined "protocol'', humans may still make mistakes. A common countermeasure is to have multiple people review the same content; however, this process is often time-intensive and requires accurate aggregation of potentially noisy decisions.