Machine studying (ML) and synthetic intelligence (AI) methods rely closely on human-annotated information for coaching and analysis. A serious problem on this context is the prevalence of annotation errors, as their results can degrade mannequin efficiency. This paper presents a predictive error mannequin educated to detect potential errors in search relevance annotation duties for 3 industry-scale ML functions (music streaming, video streaming, and cell apps). Drawing on real-world information from an intensive search relevance annotation program, we display that errors may be predicted with reasonable mannequin efficiency (AUC=0.65-0.75) and that mannequin efficiency generalizes properly throughout functions (i.e., a world, task-agnostic mannequin performs on par with task-specific fashions). In distinction to previous analysis, which has usually centered on predicting annotation labels from task-specific options, our mannequin is educated to foretell errors instantly from a mixture of job options and behavioral options derived from the annotation course of, to be able to obtain a excessive diploma of generalizability. We display the usefulness of the mannequin within the context of auditing, the place prioritizing duties with excessive predicted error chances significantly will increase the quantity of corrected annotation errors (e.g., 40% effectivity good points for the music streaming software). These outcomes spotlight that behavioral error detection fashions can yield appreciable enhancements within the effectivity and high quality of knowledge annotation processes. Our findings reveal important insights into efficient error administration within the information annotation course of, thereby contributing to the broader discipline of human-in-the-loop ML.