Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction
This work addresses fairness in high-stakes criminal justice applications, offering a generalizable method to reduce bias, though it is incremental as it builds on existing adversarial learning approaches.
The researchers tackled racial bias in recidivism prediction scores, such as COMPAS, by developing an adversarially-trained neural network that improved predictive accuracy and achieved better fairness on two out of three measures compared to existing methods.
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.