Alexis Morin-Martel navigates the intricate landscape of judicial decision-making and advances the concept of Judge Assistance Systems (JAS), proposing it as a tool for enhancing the trustworthiness of judges in bail decisions. The argument is grounded in the relational theory of procedural justice, which emphasizes the role of trust, voice, neutrality, and respect in the administration of justice. The research underpins its analysis through an exploration of the nuanced terrain of trustworthiness, distinguishing between actual and rich trustworthiness, and articulating the potential role of JAS in amplifying both.
The author leverages the empirical study by Kleinberg et al. (2017a) to illustrate how JAS, equipped with complex algorithms, can assist judges in making more precise bail decisions, thereby enhancing their actual trustworthiness. A key idea espoused is the potential for JAS to act as a check on judicial decision-making, allowing judges to reconsider decisions that deviate significantly from statistical norms. However, the author acknowledges that the implementation of JAS should not undermine the principle of voice, one of the pillars of relational justice, ensuring that defendants have the opportunity to influence the decision-making process.
Further, the study takes into account the perceived trustworthiness of judges when using a JAS. It acknowledges the inherent public skepticism towards algorithmic decisions, often due to their perceived opacity. The argument is made that focusing on accuracy, rather than transparency, of these algorithms is more likely to enhance perceived trustworthiness. Importantly, the author suggests that regular audits within legal institutions could effectively monitor the accuracy of JAS, thus reinforcing public trust over time. However, the author admits that while the ‘voice’ and ‘neutrality’ criteria could likely be met by JAS, its ability to meet the ‘respect’ requirement remains uncertain and needs further examination.
The research article finds a nexus with broader philosophical themes, particularly those concerning human-machine interaction and the ethical implications of algorithmic decision-making. The proposal of JAS as a tool to enhance judicial trustworthiness is reflective of the broader trend towards technocratic governance. This trend raises critical questions about the balance between human judgment and algorithmic precision, and the philosophical implications of delegating traditionally human tasks to artificial intelligence. Moreover, the emphasis on accuracy over transparency in JAS echoes the larger debate on the ethical trade-offs in AI applications, especially in high-stake public decisions.
Future research could explore several intriguing avenues. The extension of JAS to other areas of judicial decision-making, beyond bail decisions, could be considered. Studies could also focus on the development of more transparent and interpretable models without compromising accuracy, addressing public distrust of ‘black box’ algorithms. Furthermore, future research might investigate the potential impact of JAS on other aspects of the relational theory of procedural justice, particularly the ‘respect’ requirement. Lastly, empirical studies evaluating the effectiveness and reliability of JAS in real-world court settings could provide valuable insights into the practicality of implementing such systems.
Abstract
The use of AI algorithms in criminal trials has been the subject of very lively ethical and legal debates recently. While there are concerns over the lack of accuracy and the harmful biases that certain algorithms display, new algorithms seem more promising and might lead to more accurate legal decisions. Algorithms seem especially relevant for bail decisions, because such decisions involve statistical data to which human reasoners struggle to give adequate weight. While getting the right legal outcome is a strong desideratum of criminal trials, advocates of the relational theory of procedural justice give us good reason to think that fairness and perceived fairness of legal procedures have a value that is independent from the outcome. According to this literature, one key aspect of fairness is trustworthiness. In this paper, I argue that using certain algorithms to assist bail decisions could increase three different aspects of judges’ trustworthiness: (1) actual trustworthiness, (2) rich trustworthiness, and (3) perceived trustworthiness.
Machine learning in bail decisions and judges’ trustworthiness