Gregory H. Fox and Brad R. Roth
Gregory H. Fox and Brad R. Roth
Kevin D. Ashley
AI and law computational models of legal argument provide an empirical methodology for investigating the role of values in legal reasoning. These methods include assigning context-sensitive quantitative weights to values using argument schemes for generating case-based arguments and predictions, turning information such as values, legal concepts, or chronological order on and off to assess effects on predictive accuracy, testing arguments and predictions with hypotheticals that modify applicable value effects, and supporting testing hypotheses about cases and values. As such, these AI and law models complement jurisprudential theorizing about values and contribute to computational legal studies.
This chapter provides an overview and illustration of agent-based modeling, a computational method for building “bottom up” simulations to investigate how the actions and interactions of individual autonomous units (or “agents”) generate systemic effects. The chapter explains the basic nature and purposes of agent-based modeling, distinguishes agent-based modeling from other methods, and canvasses several ways in which agent-based modeling has been applied to legal studies. It goes on to present a basic version of an original agent-based model of judicial review to illustrate how the method might be used to shed fresh light on old questions in comparative constitutional law.
Janis Beckedorf, Dirk Hartung and Phillip Sittig
Both access to court decisions and collaboration of lawyers and computer scientists are limited in Germany. The University of Hamburg and Bucerius Law School join forces to address these issues as described in the introduction. The second part of this chapter gives an overview of access to court decisions in Germany. In the next part we describe a didactic concept to provide students of both fields with fundamental knowledge and an experience of collaboration with people from the other domain. In the final part, the chapter provides details of one project executed in one of the classes based on this concept. The project consists of a rule-based information extraction, a machine learning-based outcome classification and a statistical description of 56,288 decisions of the highest German civil and criminal law.
Nischal Mainali, Liam Meier, Elliott Ash and Daniel L. Chen
What modes of moral reasoning do judges employ? We attempt to automatically classify moral reasoning with a linear Support Vector Machine (SVM) trained on applied ethics articles. The model classifies paragraphs of text in holdout data with over 90 percent accuracy. We then apply the classifier to a corpus of circuit court opinions and find a significant increase in consequentialist reasoning over time. We report rankings of relative use of reasoning modes by legal topic, by judge, and by judge law school. Though statistical techniques inherently face significant limitations in this task, we show some of the promise of machine learning for understanding human moral reasoning.
In this chapter, I examine various citizenship templates following the activation of Article 50 TEU by the United Kingdom in 2017. By combining top-down and bottom-up perspectives, that is, ‘how institutions think’ or ‘seeing like a state’ as well as citizens’ views and vision, I discuss possible policy options concerning the status of EU citizens affected by Brexit and differentiated citizenship arrangements (a scala civium) and argue that there is room for institutional innovation in the domain of citizenship. I present, and defend, the proposal for a special EU protected citizen status for both EU citizens living in the UK and UK nationals living in other Member States while the final section contains the concluding remarks.