No abstract

The prediction of legal outcomes and other legal domain related variables has served as the basis of a number of recent studies. While recent studies have estimated standardised variables and dichotomous outcomes such as the outcome of a judicial decision process, few studies have employed dichotomous data and categorical data to predict the basis of a legal defense strategy or the likelihood of trial success. Empirical research within the judicial sciences continues to employ a limited subset of empirical methods. This article reasserts the benefits of several artificial intelligence based non-parametric techniques that are better suited to the discipline than many of the common methods employed within the literature. The article considers the predictability of fair use defense within the U.S. during copyright infringement proceedings, and the likelihood of trial success.

Can advocacy experience differentials be used in formulating a model to predict trial outcomes in the US Supreme Court? In recent years, a number of studies have considered the role of experience before the Supreme Court in the determination of trial outcomes. The work of Sheehan, Mishler and Songer supports the assertion that trial experience possessed by trial lawyers is associated with disproportionate rates of success. McGuire is a significant study into the impact of the experience of competing trial lawyers on judicial decision making. The study identified the experience differentials of lawyers and sought to determine the impact of these differentials on trial outcomes. The study found that trial experience possessed by trial lawyers was associated with favourable trial outcomes. The current study extends upon McGuire, assessing the robustness of the original study employing a series of more advanced parametric estimation techniques. The study then uses the McGuire logistic model framework to develop a model of prediction, employing a backward propagation, multilayer perceptron network model.

The present article considers the importance of legal system origin in compliance with ‘international soft law,' or normative provisions contained in non-binding texts. The study considers key economic and governance metrics on national acceptance an implementation of the first Basle accord. Employing a data set of 70 countries, the present study considers the role of market forces and bilateral and multi-lateral pressures on implementation of soft law. There is little known about the role of legal system structure-related variables as factors moderating the implementation of multi-lateral agreements and international soft law, such as the 1988 accord. The present study extends upon research within the extant literature by employing a novel estimation method, a neural network modelling technique, with multi-layer perceptron artificial neural network (MPANN). Consistent with earlier studies, the article identifies a significant and positive effect associated with democratic systems and the implementation of the Basle accord. However, extending upon traditional estimation techniques, the study identifies the significance of savings rates and government effectiveness in determining implementation. Notably, the method is able to achieve a superior goodness of fit and predictive accuracy in determining implementation.

Automated decision support systems with high stake decision processes are frequently controversial. The Online Compliance Intervention (herewith “OCI” or “RoboDebt”) is a system of compliance implemented with the intention to facilitate automatic issuance of statutory debt notices to individuals, taking a receipt of welfare payments and exceeding their entitlement. The system appears to employ rudimentary data scraping and expert systems to determine whether notices should be validly issued. However, many individuals that take receipt of debt notices assert that they were issued in error. The commentary on the system has resulted in a lot of conflation of the system with other system types and caused many to question the role of decision of support systems in public administration given the potentially deleterious impacts of such systems for the most vulnerable. The authors employ a taxonomy of Robotic Process Automation (RPA) issues, to review the OCI and RPA more generally. This paper identifies potential problems of bias, inconsistency, procedural fairness, and overall systematic error. This research also considers a series of RoboDebt specific issues regarding contractor arrangements and the potential impact of the system for Australia's Indigenous population. The authors offer a set of recommendations based on the observed challenges, emphasizing the importance of moderation, independent algorithmic audits, and ongoing reviews. Most notably, this paper emphasizes the need for greater transparency and a broadening of criteria to determine vulnerability that encompasses, temporal, geographic, and technological considerations.