  Predictive models such as decision trees and neural networks may produce
discrimination in their predictions. This paper proposes a method to
post-process the predictions of a predictive model to make the processed
predictions non-discriminatory. The method considers multiple protected
variables together. Multiple protected variables make the problem more
challenging than a simple protected variable. The method uses a well-cited
discrimination metric and adapts it to allow the specification of explanatory
variables, such as position, profession, education, that describe the contexts
of the applications. It models the post-processing of predictions problem as a
nonlinear optimization problem to find best adjustments to the predictions so
that the discrimination constraints of all protected variables are all met at
the same time. The proposed method is independent of classification methods. It
can handle the cases that existing methods cannot handle: satisfying multiple
protected attributes at the same time, allowing multiple explanatory
attributes, and being independent of classification model types. An evaluation
using four real world data sets shows that the proposed method is as
effectively as existing methods, in addition to its extra power.
