  We develop a fully Bayesian, logistic tracking algorithm with the purpose of
providing classification results that are unbiased when applied uniformly to
individuals with differing sensitive variable values. Here, we consider bias in
the form of differences in false prediction rates between the different
sensitive variable groups. Given that the method is fully Bayesian, it is well
suited for situations where group parameters or logistic regression
coefficients are dynamic quantities. We illustrate our method, in comparison to
others, on both simulated datasets and the well-known ProPublica COMPAS
dataset.
