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 our related work [1], we have studied 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 in this work 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 have been applied to analyze the ML-based predictions in terms of statistical analysis.

This research work, conducted by the UAEGEAN (i-Lab), inspired us to prepare the research proposal funded by the DG JUST, setting the foundations for FAIR-PReSONS project.

Article provided by the University of the Aegean

References
[1] S. Bentos et al., “Evaluation of Fairness in Machine Learning-Based Recidivism Predictions: The Case of Greek Female Prison System,” 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), FEZ, Morocco, 2024, pp. 1-8, doi:10.1109/IRASET60544.2024.10548202