This algorithmic bias is particularly useful for weakly supervised learners (such as EM) that optimize an objective function other than classification accuracy and can potentially produce a classification that is substantially different from the actual one.
fS-EM attempts to ensure that the classification produced by the weakly supervised learner weakly agrees with the actual classification, where the weak disagreement rate between two classifications is defined as the difference between their positive class priors.