Law JournalFeeds

This paper studies how legal choices, and in particular legislative determinations, need to consider multiple rights and values,
and can be assessed accordingly. First it is argued that legal norms (and in particular constitutional right-norms) often
prescribe the pursuit of goals, which may be in conflict one with another. Then a model of teleological reasoning is brought
to bear on choices affecting different goals, among which those prescribed by constitutional norms. An analytical framework
is provided for evaluating such choices with regard to possible alternatives. The assessment of legislative choices according
to proportionality is then considered, and is modelled using the provided analytical framework. Finally, the framework is
expanded to include the ideas of reasonableness and institutional deference, and the corresponding margins of appreciation.

This paper studies the use of hypothetical and value-based reasoning in US Supreme-Court cases concerning the United States
Fourth Amendment. Drawing upon formal AI & Law models of legal argument a semi-formal reconstruction is given of parts of
the Carney case, which has been studied previously in AI & law research on case-based reasoning. As part of the reconstruction, a semi-formal
proposal is made for extending the formal AI & Law models with forms of metalevel reasoning in several argument schemes. The
result is compared with Rissland’s (1989) analysis in terms of dimensions and Ashley’s (2008) analysis in terms of his process model of legal argument with hypotheticals.

The effectiveness of information retrieval technology in electronic discovery (E-discovery) has become the subject of judicial
rulings and practitioner controversy. The scale and nature of E-discovery tasks, however, has pushed traditional information
retrieval evaluation approaches to their limits. This paper reviews the legal and operational context of E-discovery and the
approaches to evaluating search technology that have evolved in the research community. It then describes a multi-year effort
carried out as part of the Text Retrieval Conference to develop evaluation methods for responsive review tasks in E-discovery.
This work has led to new approaches to measuring effectiveness in both batch and interactive frameworks, large data sets,
and some surprising results for the recall and precision of Boolean and statistical information retrieval methods. The paper
concludes by offering some thoughts about future research in both the legal and technical communities toward the goal of reliable,
effective use of information retrieval in E-discovery.

This paper presents a theory of reasoning with evidence in order to determine the facts in a criminal case. The focus is on
the process of proof, in which the facts of the case are determined, rather than on related legal issues, such as the admissibility
of evidence. In the literature, two approaches to reasoning with evidence can be distinguished, one argument-based and one
story-based. In an argument-based approach to reasoning with evidence, the reasons for and against the occurrence of an event,
e.g., based on witness testimony, are central. In a story-based approach, evidence is evaluated and interpreted from the perspective
of the factual stories as they may have occurred in a case, e.g., as they are defended by the prosecution. In this paper,
we argue that both arguments and narratives are relevant and useful in the reasoning with and interpretation of evidence.
Therefore, a hybrid approach is proposed and formally developed, doing justice to both the argument-based and the narrative-based
perspective. By the formalization of the theory and the associated graphical representations, our proposal is the basis for
the design of software developed as a tool to make sense of the evidence in complex cases.

Given the very large numbers of documents involved in e-discovery investigations, lawyers face a considerable challenge of
collaborative sensemaking. We report findings from three workplace studies which looked at different aspects of how this challenge
was met. From a sociotechnical perspective, the studies aimed to understand how investigators collectively and individually
worked with information to support sensemaking and decision making. Here, we focus on discovery-led refinement; specifically,
how engaging with the materials of the investigations led to discoveries that supported refinement of the problems and new
strategies for addressing them. These refinements were essential for tractability. We begin with observations which show how
new lines of enquiry were recursively embedded. We then analyse the conceptual structure of a line of enquiry and consider
how reflecting this in e-discovery support systems might support scalability and group collaboration. We then focus on the
individual activity of manual document review where refinement corresponded with the inductive identification of classes of
irrelevant and relevant documents within a collection. Our observations point to the effects of priming on dealing with these
efficiently and to issues of cognitive ergonomics at the human–computer interface. We use these observations to introduce
visualisations that might enable reviewers to deal with such refinements more efficiently.

This paper describes a tool for assisting lawyers and paralegal teams during document review in eDiscovery. The tool combines
a machine learning technology (CategoriX) and advanced multi-touch interface capable of not only addressing the usual cost,
time and accuracy issues in document review, but also of facilitating the work of the review teams by capitalizing on the
intelligence of the reviewers and enabling collaborative work.

We present a reference model for finding (prima facie) evidence of discrimination in datasets of historical decision records
in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. We formalize
the process of direct and indirect discrimination discovery in a rule-based framework, by modelling protected-by-law groups,
such as minorities or disadvantaged segments, and contexts where discrimination occurs. Classification rules, extracted from
the historical records, allow for unveiling contexts of unlawful discrimination, where the degree of burden over protected-by-law
groups is evaluated by formalizing existing norms and regulations in terms of quantitative measures. The measures are defined
as functions of the contingency table of a classification rule, and their statistical significance is assessed, relying on
a large body of statistical inference methods for proportions. Key legal concepts and reasonings are then used to drive the
analysis on the set of classification rules, with the aim of discovering patterns of discrimination, either direct or indirect.
Analyses of affirmative action, favoritism and argumentation against discrimination allegations are also modelled in the proposed
framework. Finally, we present an implementation, called LP2DD, of the overall reference model that integrates induction,
through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical
tools. The LP2DD system is put at work on the analysis of a dataset of credit decision records.

Legal judgments are complex in nature and hence a brief summary of the judgment, known as a headnote, is generated by experts to enable quick perusal. Headnote generation is a time consuming process and there have been attempts
made at automating the process. The difficulty in interpreting such automatically generated summaries is that they are not
coherent and do not convey the relative relevance of the various components of the judgment. A legal judgment can be segmented
into coherent chunks based on the rhetorical roles played by the sentences. In this paper, a comprehensive system is proposed for labeling sentences with their rhetorical roles
and extracting structured head notes automatically from legal judgments. An annotated data set was created with the help of
legal experts and used as training data. A machine learning technique, Conditional Random Field, is applied to perform document
segmentation by identifying the rhetorical roles. The present work also describes the application of probabilistic models
for the extraction of key sentences and composing the relevant chunks in the form of a headnote. The understanding of basic
structures and distinct segments is shown to improve the final presentation of the summary. Moreover, by adding simple additional
features the system can be extended to other legal sub-domains. The proposed system has been empirically evaluated and found
to be highly effective on both the segmentation and summarization tasks. The final summary generated with underlying rhetorical
roles improves the readability and efficiency of the system.

The question of liability in the case of using intelligent agents is far from simple, and cannot sufficiently be answered
by deeming the human user as being automatically responsible for all actions and mistakes of his agent. Therefore, this paper
is specifically concerned with the significant difficulties which might arise in this regard especially if the technology
behind software agents evolves, or is commonly used on a larger scale. Furthermore, this paper contemplates whether or not
it is possible to share the responsibility with these agents and what are the main objections surrounding the assumption of
considering such agents as responsible entities. This paper, however, is not intended to provide the final answer to all questions
and challenges in this regard, but to identify the main components, and provide some perspectives on how to deal with such
issue.

Combating the identity problem is crucial and urgent as false identity has become a common denominator of many serious crimes,
including mafia trafficking and terrorism. Without correct identification, it is very difficult for law enforcement authority
to intervene, or even trace terrorists’ activities. Amongst several identity attributes, personal names are commonly, and
effortlessly, falsified or aliased by most criminals. Typical approaches to detecting the use of false identity rely on the
similarity measure of textual and other content-based characteristics, which are usually not applicable in the case of highly
deceptive, erroneous and unknown descriptions. This barrier can be overcome through analysis of link information displayed
by the individual in communication behaviours, financial interactions and social networks. In particular, this paper presents
a novel link-based approach that improves existing techniques by integrating multiple link properties in the process of similarity
evaluation. It is utilised in a hybrid model that proficiently combines both text-based and link-based measures of examined
names to refine the justification of their similarity. This approach is experimentally evaluated against other link-based
and text-based techniques, over a terrorist-related dataset, with further generalization to a similar problem occurring in
publication databases. The empirical study demonstrates the great potential of this work towards developing an effective identity
verification system.