Here we present an example using the ProPublica COMPAS data set. Using race as the protected attribute, we compare raw and debiased predictions trained on a simple logistic regression model. We show that the debiased data decreases the magnitude of the difference of both false positve and false negative rates between African-Americans and Caucasians will little loss to accuracy. While our model is simple, we also show it is comparable to the COMPAS algorithm in scoring and accuracy.