This paper explores the application of artificial intelligence (AI) in predicting recidivism among offenders, examining both the potential benefits and ethical concerns. We examine various machine learning models used for recidivism prediction. Each approach presents unique advantages and limitations in terms of accuracy, transparency, and real-world application. For instance, while some models may achieve high predictive accuracy, they often lack interpretability, making it difficult for judges and parole officers to fully trust the predictions. Conversely, more interpretable models might compromise some accuracy but offer clearer insights into how predictions are generated. A key focus of the paper is on the legislative frameworks guiding AI use in the criminal justice sphere. We compare the approaches taken in the United States and Europe, noting how differing legal and ethical standards shape the development of AI systems. In the U.S., AI tools have prompted significant debate regarding accountability and discrimination, especially given the history of bias within the system. In contrast, European nations often prioritize data protection and privacy, influencing their methodology for implementing predictive models. We also address the critical issue of bias within AI systems. Historical data used for training these models can perpetuate existing bias and potentially lead to disproportionate predictions for certain demographics. Finally, we discuss the need for interdisciplinary collaboration among technologists, legal experts, and ethicists in developing fair AI applications. This paper advocates for responsible deployment of AI tools in predicting recidivism, ensuring that they enhance, rather than undermine, the justice system.
Feuerbach et al. (Wed,) studied this question.