  Recidivism prediction scores are used across the USA to determine sentencing
and supervision for hundreds of thousands of inmates. One such generator of
recidivism prediction scores is Northpointe's Correctional Offender Management
Profiling for Alternative Sanctions (COMPAS) score, used in states like
California and Florida, which past research has shown to be biased against
black inmates according to certain measures of fairness. To counteract this
racial bias, we present an adversarially-trained neural network that predicts
recidivism and is trained to remove racial bias. When comparing the results of
our model to COMPAS, we gain predictive accuracy and get closer to achieving
two out of three measures of fairness: parity and equality of odds. Our model
can be generalized to any prediction and demographic. This piece of research
contributes an example of scientific replication and simplification in a
high-stakes real-world application like recidivism prediction.
