  Procedural fairness has been a public concern, which leads to controversy
when making decisions with respect to protected classes, such as race, social
status, and disability. Some protected classes can be inferred according to
some safe proxies like surname and geolocation for the race. Hence, implicitly
utilizing the predicted protected classes based on the related proxies when
making decisions is an efficient approach to circumvent this issue and seek
just decisions. In this article, we propose a hierarchical random forest model
for prediction without explicitly involving protected classes. Simulation
experiments are conducted to show the performance of the hierarchical random
forest model. An example is analyzed from Boston police interview records to
illustrate the usefulness of the proposed model.
