Algorithms play an essential and expanding role in public policy decisions, including those in criminal justice. This short paper reports on the first author’s summer research project characterizing the tradeoff between accuracy and fairness in parole decision predictions. The dataset employed in this study contains over 30,000 parole decisions made by the New York State Division of Criminal Justice Services. Each decision contains information on the subject, such as sex, race/ethnicity, and parole decision, as well as predictive features describing the crime committed by the subject and the parole interview held. Logistic regression, decision tree, support vector machine, and random forest models are trained and utilized to analyze parole decision predictions based on the available features. Most models fail to pass standard fairness tests for most fairness metrics. Moreover, while there may be an overall tradeoff between fairness and accuracy, the obtained differences in accuracy are too small to make a well-supported claim. Future research may enhance the preliminary work introduced in this paper by using multiple real-world datasets to investigate the tradeoff between accuracy and fairness.

Criminal recidivism models are tools that have gained widespread adoption by parole boards across the United States to assist with parole decisions. These models take in large amounts of data about an individual and then predict whether an individual would commit a crime if released on parole. Although such models are not the only or primary factor in making the final parole decision, questions have been raised about their accuracy, fairness, and interpretability. In this paper, various machine learning-based criminal recidivism models are created based on a real-world parole decision dataset from the state of Georgia in the United States. The recidivism models are comparatively evaluated for their accuracy, fairness, and interpretability. It is found that there are noted differences and trade-offs between accuracy, fairness, and being inherently interpretable. Therefore, choosing the best model depends on the desired balance between accuracy, fairness, and interpretability, as no model is perfect or consistently the best across different criteria.