We introduce Fisher consistency in the sense of unbiasedness as a criterion
to distinguish potentially suitable and unsuitable estimators of prior class
probabilities in test datasets under prior probability and more general dataset
shift. The usefulness of this unbiasedness concept is demonstrated with three
examples of classifiers used for quantification: Adjusted Classify & Count,
EM-algorithm and CDE-Iterate. We find that Adjusted Classify & Count and
EM-algorithm are Fisher consistent. A counter-example shows that CDE-Iterate is
not Fisher consistent and, therefore, cannot be trusted to deliver reliable
estimates of class probabilities.