Understanding Annotator Safety Policy with Interpretability

Apple researchers have introduced a new interpretability method designed to identify why human annotators disagree when evaluating AI safety policies. By analyzing whether disagreements stem from task confusion or vague policy language, the system helps developers refine instructions and improve the consistency of safety training data. This approach aims to reduce errors in model development by pinpointing specific areas where labeling guidelines are either poorly understood or inherently ambiguous.