Dimensions and values for legal CBR

We build on two recent attempts to formalise reasoning with dimensions which effectively map dimensions into factors. These enable propositional reasoning, but sometimes a balance between dimensions needs to be struck, and to permit trade offs we need to keep the magnitudes and so reason more geometrically. We discuss dimensions and values, arguing that values can play several distinct roles, both explaining preferences between factors and indicating the purposes of the law

A Note on Hierarchical Constraints

In recent years a considerable amount of research has been devoted to formal theories of precedential constraint. In this note I consider a recent paper which explores the use of factor hierarchies in this connection. In that work it was shown both that cases constrained with the use of a hierarchy may be unconstrained if the hierarchy is flattened, and that cases unconstrained with a hierarchy may be constrained when the hierarchy is flattened. I discuss the nature of factor hierarchies and attempt to explain these results.</jats:p

Lessons rom implementing factors with magnitude

We discuss the lessons learned from implementing a CATO style system using factors with magnitude. In particular we identify that giving factors magnitudes enables a diversity of reasoning styles and arguments. We distinguish a variety of ways in which factors combine to determine abstract factors. We discuss several different roles for values. Finally we identify the additional value related information required to produce a working program: thresholds and weights as well as a simple preference ordering

Machine learning and legal argument

Although the argumentation justifying decisions in particular cases has always been central to AI and Law, it has recently become a burning issue as black box machine learning approaches become prevalent. In this paper we review the understanding of legal argument that has been developed in AI and Law, and indicate the most appropriate ways in which Machine Learning approaches can contribute to legal argument. We identify some key questions that must be explored to provide acceptable explanations for legal ML systems. This provides the context and directions of our current research project

A formal analysis of some factor- and precedent-based accounts of precedential constraint

In this paper several recent factor- and dimension-based models of precedential constraint are formally investigated and an alternative dimension-based model is proposed. Simple factor- and dimension-based syntactic criteria are identified for checking whether a decision in a new case is forced, in terms of the relevant differences between a precedent and a new case, and the difference between absence of factors and negated factors in factor-based models is investigated. Then Horty’s and Rigoni’s recent dimension-based models of precedential constraint are critically examined. An alternative to their reason models is proposed which is less expressive but arguably easier to apply in practice

Thirty years of Artificial Intelligence and Law:the second decade

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments

Using Issues to Explain Legal Decisions.

The need to explain the output from Machine Learning systems designed to predict the outcomes of legal cases has led to a renewed interest in the explanations offered by traditional AI and Law systems, especially those using factor based reasoning and precedent cases. In this paper we consider what sort of explanations we should expect from such systems, with a particular focus on the structure that can be provided by the use of issues in cases.Comment: Presented at the XAILA workshop 202