Evaluation of Fairness in Machine Learning-Based Recidivism Predictions: the Case of Greek Female Prison System

Recidivism refers to a person's relapse into criminal behavior, often after receiving some form of punishment or undergoing intervention for a previous crime. Machine learning (ML) algorithms are commonly used for quantitatively predicting recidivism by assessing a criminal defendant's likelihood of committing a crime thus, guiding decisions and imposing choices for criminal justice officers in managing the criminal population. Beyond the prediction adequacy of these algorithms, an important issue is whether they are capable of making fair decisions. It has been stated that attributes such as gender, race, age, ethnicity, and unemployment appear to affect the fair decision-making of ML systems upon recidivism. In this paper, we study the recidivism predictions obtained by several supervised ML algorithms over a dataset that has been extracted from a Greek female prison data record. The main points addressed by the current contribution concern the study of the resulting recidivism predictions from the perspective of fairness assessment that is related to certain data attributes such as age at exiting the first imprisonment, and employment status at the moment of the first imprisonment. To accomplish that task, several criteria are applied to analyze the ML-based predictions in terms of statistical analysis.