  Large technology firms face the problem of moderating content on their online
platforms for compliance with laws and policies. To accomplish this at the
scale of billions of pieces of content per day, a combination of human and
machine review are necessary to label content. Subjective judgement and human
bias are of concern to both human annotated content as well as to auditors who
may be employed to evaluate the quality of such annotations in conformance with
law and/or policy. To address this concern, this paper presents a novel
application of statistical analysis methods to identify human error and these
sources of audit risk.
