Towards Fair Recidivism Prediction: Addressing Bias in Machine Learning for the Greek Prison System

Recidivism prediction has become an essential tool in criminal justice systems, aiding decision-making in areas such as sentencing, parole, and rehabilitation. Machine learning (ML) algorithms have been widely employed to improve the accuracy of recidivism risk assessments. However, concerns about fairness and algorithmic bias have been raised, particularly in high-stakes applications. This study focuses on the Greek prison system, utilizing a dataset from Greek prisons to analyze and mitigate biases in ML-based recidivism predictions. The study primarily investigates the impact of age as a sensitive attribute and employs fairness-aware optimization techniques to reduce disparities in predictive outcomes. By incorporating fairness constraints into the training process, we demonstrate that balancing fairness and accuracy is possible. The results indicate that implementing fairness-aware ML models can significantly reduce bias, particularly against younger offenders, while maintaining acceptable predictive performance. Our findings contribute to ongoing discussions on the ethical application of AI in criminal justice and highlight the necessity of fairness-aware methodologies for equitable decision-making.