Tag: Law

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(Featured) Machine learning in bail decisions and judges’ trustworthiness

Machine learning in bail decisions and judges’ trustworthiness

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
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(Featured) The black box problem revisited. Real and imaginary challenges for automated legal decision making

The black box problem revisited. Real and imaginary challenges for automated legal decision making

Bartosz Brożek et al. explore the ethical and practical dilemmas arising from the integration of Artificial Intelligence (AI) in the realm of law. The authors suggest that despite the perceived opacity and unpredictability of AI, these machines can provide rational and justifiable decisions in legal reasoning. By challenging conventional notions of decision-making and justifiability, the paper reframes the discussion around AI’s role in law and provides a compelling argument for AI’s potential to aid in legal reasoning.

The authors delve into the intricacies of legal decision-making, highlighting the contrast between our traditional expectations and the realities of legal reasoning. They argue that while we expect legal decisions to be based on clearly identifiable structures, algorithmic operations on beliefs, and classical logic, the cognitive science research paints a contrasting picture. The authors further suggest that most legal decisions emerge unconsciously, lack a recognizable structure, and are often influenced by emotional reactions and social training. This observation paves the way for a paradigm shift, suggesting that rather than the process, it is the justifiability of the decision ex post that is paramount.

The authors propose a two-module AI system, one intuitive and the other rational. The intuitive module, powered by machine learning, recognizes patterns from large datasets and makes decisions. The rational module, grounded in logic, does not make decisions but justifies those made by the intuitive module. In this framework, AI can be seen as rational if an acceptable justification can be provided for its decisions, despite their unpredictability. This interesting intertwining of machine learning and logic reshapes our understanding of AI’s role in legal decision-making.

This paper touches upon broader philosophical issues surrounding consciousness, rationality, and decision-making. By arguing for a shift from a process-oriented to a result-oriented evaluation of decision-making, the authors challenge the traditional Kantian perspective. The proposed model, in which an AI’s decisions are assessed based on their post-hoc justifiability, aligns more closely with consequentialist philosophy. This emphasis on the end result rather than the means to reach it further stimulates the ongoing debate on the ethical implications of AI use and the re-evaluation of long-held philosophical tenets in the face of technological advancements.

Future research could explore various facets of this proposed two-module AI system, particularly the interplay and potential conflicts between the intuitive and rational modules. Questions around what constitutes an “acceptable justification” in various legal contexts also demand further exploration. Additionally, research could investigate how this approach to AI in law would intersect with other legal principles, such as fairness, transparency, and due process. Ultimately, the paper presents a compelling case for rethinking the role and evaluation of AI in legal decision-making, opening up intriguing possibilities for future philosophical and legal discourse.

Abstract

This paper addresses the black-box problem in artificial intelligence (AI), and the related problem of explainability of AI in the legal context. We argue, first, that the black box problem is, in fact, a superficial one as it results from an overlap of four different – albeit interconnected – issues: the opacity problem, the strangeness problem, the unpredictability problem, and the justification problem. Thus, we propose a framework for discussing both the black box problem and the explainability of AI. We argue further that contrary to often defended claims the opacity issue is not a genuine problem. We also dismiss the justification problem. Further, we describe the tensions involved in the strangeness and unpredictability problems and suggest some ways to alleviate them.

The black box problem revisited. Real and imaginary challenges for automated legal decision making
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(Featured) Bare statistical evidence and the legitimacy of software-based judicial decisions

Bare statistical evidence and the legitimacy of software-based judicial decisions

Eva Schmidt et al. explore the question of whether evidence provided by software systems can serve as a legitimate basis for judicial decisions, focusing on two primary cases: recidivism predictions and DNA cold hit cases. The authors approach this question by analyzing the nature of bare statistical evidence and its relation to individualized evidence. They argue that while bare statistical evidence is generally considered insufficient to meet the standard of proof in criminal and civil cases, software-generated evidence can be individualized and, thus, meet this standard of proof under certain conditions.

In the case of recidivism predictions, the authors discuss the use of software systems such as COMPAS, which rely on bare statistical evidence to estimate the risk of an individual reoffending. They argue that for a sentence to be just and have the potential to serve as an incentive, it must be based on the specific features of the individual concerned, rather than solely on general features of a group that they belong to, which may correlate with high recidivism risk. The authors maintain that bare statistical evidence alone is insufficient for sentencing decisions.

Regarding DNA cold hit cases, the authors propose that statistical evidence generated by software systems like TrueAllele can be individualized through abductive reasoning or inference to the best explanation when it comes to cases of extreme probability. They argue that when the best explanation for the evidence is that the defendant is the source of the crime scene DNA, the evidence can be considered individualized and, thus, meet the standard of proof for criminal cases. This aligns with the normic account of individualized evidence, which posits that the best explanation of a piece of evidence is also the most normal explanation.

The authors’ analysis raises broader philosophical questions concerning the nature of evidence, the role of statistical reasoning in judicial decision-making, and the ethical implications of using software systems in the courtroom. It highlights the importance of distinguishing between different types of support, such as abductive (normic) support and probabilistic support, and of understanding the connections and disconnections between these concepts. Moreover, the paper touches on issues related to transparency, explainability, and fairness in the use of software systems as decision-making aids in the legal context.

Future research could further explore the implications of using software systems for other types of legal evidence and decision-making processes, as well as the ethical and epistemological challenges that these systems pose. Additionally, investigating the relationship between individualized evidence and statistical reasoning could shed light on the nature of evidence itself and the standards of proof required in various legal contexts. Finally, future work could focus on the development of guidelines and best practices for the implementation and evaluation of software systems in the courtroom, addressing issues such as transparency, explainability, and the appropriate weighting of statistical and individualized evidence.

Abstract

Can the evidence provided by software systems meet the standard of proof for civil or criminal cases, and is it individualized evidence? Or, to the contrary, do software systems exclusively provide bare statistical evidence? In this paper, we argue that there are cases in which evidence in the form of probabilities computed by software systems is not bare statistical evidence, and is thus able to meet the standard of proof. First, based on the case of State v. Loomis, we investigate recidivism predictions provided by software systems used in the courtroom. Here, we raise problems for software systems that provide predictions that are based on bare statistical evidence. Second, by examining the case of People v. Chubbs, we argue that the statistical evidence provided by software systems in cold hit DNA cases may in some cases suffice for individualized evidence, on a view on which individualized evidence is evidence that normically supports the relevant proposition (Smith, in Mind 127:1193–1218, 2018).

Bare statistical evidence and the legitimacy of software-based judicial decisions