  Deep learning crime predictive tools use past crime data and additional
behavioral datasets to forecast future crimes. Nevertheless, these tools have
been shown to suffer from unfair predictions across minority racial and ethnic
groups. Current approaches to address this unfairness generally propose either
pre-processing methods that mitigate the bias in the training datasets by
applying corrections to crime counts based on domain knowledge or in-processing
methods that are implemented as fairness regularizers to optimize for both
accuracy and fairness. In this paper, we propose a novel deep learning
architecture that combines the power of these two approaches to increase
prediction fairness. Our results show that the proposed model improves the
fairness of crime predictions when compared to models with in-processing
de-biasing approaches and with models without any type of bias correction,
albeit at the cost of reducing accuracy.
