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 Artificial Intelligence and LawJournal Prestige (SJR): 0.937 Citation Impact (citeScore): 2Number of Followers: 12      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1572-8382 - ISSN (Online) 0924-8463 Published by Springer-Verlag  [2353 journals]
• Legal and ethical implications of applications based on agreement
technologies: the case of auction-based road intersections
• Abstract: Agreement technologies refer to a novel paradigm for the construction of distributed intelligent systems, where autonomous software agents negotiate to reach agreements on behalf of their human users. Smart Cities are a key application domain for agreement technologies. While several proofs of concept and prototypes exist, such systems are still far from ready for being deployed in the real-world. In this paper we focus on a novel method for managing elements of smart road infrastructures of the future, namely the case of auction-based road intersections. We show that, even though the key technological elements for such methods are already available, there are multiple non-technical issues that need to be tackled before they can be applied in practice. For this purpose, we analyse legal and ethical implications of auction-based road intersections in the context of international regulations and from the standpoint of the Spanish legislation. From this exercise, we extract a set of required modifications, of both technical and legal nature, which need to be addressed so as to pave the way for the potential real-world deployment of such systems in a future that may not be too far away.
PubDate: 2019-09-30

• Is hybrid formal theory of arguments, stories and criminal evidence well
suited for negative causation'
• Abstract: In this paper, I have two primary goals. First, I show that the causal-based story approach in A hybrid formal theory of arguments, stories and criminal evidence (or Hybrid Theory, for short) is ill suited to negative (or absence) causation. In the literature, the causal-based approach requires that hypothetical stories be causally linked to the explanandum. Many take these links to denote physical or psychological causation, or temporal precedence. However, understanding causality in those terms, as I will show, cannot capture cases of negative causation, which are of interest to the Law. In keeping with this, I also discuss some of the difficulties Hybrid Theory invites by remaining silent on the nature of the causal links. In my second aim, I sketch a way for Hybrid Theory to overcome this problem. By replacing the original, underlying causal structure with contrastive causation in the law, Hybrid Theory can represent reasoning in which the evidence that is appealed to is causally linked via negative causation to the explananda.
PubDate: 2019-09-26

• Interactive virtue and vice in systems of arguments: a logocratic analysis
• Abstract: The Logocratic Method, and the Logocratic theory that underwrites it, provide a philosophical explanation of three purposes or goals that arguers have for their arguments: to make arguments that are internally strong (the premises follow from the conclusions, to a greater or lesser degree—greatest degree in valid deductive arguments), or that are dialectically strong (win in some forum of argument competition, as for example in litigation contests of plaintiffs or prosecutors on the one hand, and defendants, on the other), or that are rhetorically strong (effective at persuading a targeted audience). This article presents the basic terms and methods of Logocratic analysis and then uses a case study to illustrate the Logocratic explanation of arguments. Highlights of this explanation are: the use of a (non-moral) virtue (and vice) framework to explicate the three strengths and weaknesses of arguments that are of greatest interest to arguers in many contexts (including but not limited to the context of legal argument), the Logocratic explication of the structure of abduction generally and of legal abduction specifically, the concept of a system of arguments, and the concept of the dynamic interactive virtue (and vice) of arguments—a property of systems of arguments in which the system of arguments as a whole (for example, the set of several arguments typically offered by a plaintiff or by a defendant) is as virtuous (or vicious) as are the component arguments that comprise the system. This is especially important since, according to Logocratic theory (and as illustrated in detail in this paper), some arguments, such as abduction and analogical argument, are themselves comprised of different logical forms (for example, abduction always plays a role within analogical argument, and either deduction or defeasible modus ponens, always plays a role within legal abduction).
PubDate: 2019-08-10

• Appellate Court Modifications Extraction for Portuguese
• Abstract: Appellate Court Modifications Extraction consists of, given an Appellate Court decision, identifying the proposed modifications by the upper Court of the lower Court judge’s decision. In this work, we propose a system to extract Appellate Court Modifications for Portuguese. Information extraction for legal texts has been previously addressed using different techniques and for several languages. Our proposal differs from previous work in two ways: (1)  our corpus is composed of Brazilian Appellate Court decisions, in which we look for a set of modifications provided by the Court; and (2) to automatically extract the modifications, we use a traditional Machine Learning approach and a Deep Learning approach, both as alternative solutions and as a combined solution. We tackle the Appellate Court Modifications Extraction task, experimenting with a wide variety of methods. In order to train and evaluate the system, we have built the KauaneJunior corpus, using public data disclosed by the Appellate State Court of Rio de Janeiro jurisprudence database. Our best method, which is a Bidirectional Long Short-Term Memory network combined with Conditional Random Fields, obtained an $$F_{\beta = 1}$$ score of 94.79%.
PubDate: 2019-07-12

• Using machine learning to predict decisions of the European Court of Human
Rights
• Abstract: When courts started publishing judgements, big data analysis (i.e. large-scale statistical analysis of case law and machine learning) within the legal domain became possible. By taking data from the European Court of Human Rights as an example, we investigate how natural language processing tools can be used to analyse texts of the court proceedings in order to automatically predict (future) judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our (relatively simple) approach highlights the potential of machine learning approaches in the legal domain. We show, however, that predicting decisions for future cases based on the cases from the past negatively impacts performance (average accuracy range from 58 to 68%). Furthermore, we demonstrate that we can achieve a relatively high classification performance (average accuracy of 65%) when predicting outcomes based only on the surnames of the judges that try the case.
PubDate: 2019-06-26

• Evidence &amp; decision making in the law: theoretical, computational
and empirical approaches
• PubDate: 2019-06-22

• Taking stock of legal ontologies: a feature-based comparative analysis
• Abstract: Ontologies represent the standard way to model the knowledge about specific domains. This holds also for the legal domain where several ontologies have been put forward to model specific kinds of legal knowledge. Both for standard users and for law scholars, it is often difficult to have an overall view on the existing alternatives, their main features and their interlinking with the other ontologies. To answer this need, in this paper, we address an analysis of the state-of-the-art in legal ontologies and we characterise them along with some distinctive features. This paper aims to guide generic users and law experts in selecting the legal ontology that better fits their needs and in understanding its specificity so that proper extensions to the selected model could be investigated.
PubDate: 2019-06-13

• Introduction for artificial intelligence and law: special issue “natural
language processing for legal texts”
• PubDate: 2019-06-01

• Modelling competing legal arguments using Bayesian model comparison and
averaging
• Abstract: Bayesian models of legal arguments generally aim to produce a single integrated model, combining each of the legal arguments under consideration. This combined approach implicitly assumes that variables and their relationships can be represented without any contradiction or misalignment, and in a way that makes sense with respect to the competing argument narratives. This paper describes a novel approach to compare and ‘average’ Bayesian models of legal arguments that have been built independently and with no attempt to make them consistent in terms of variables, causal assumptions or parameterization. The approach involves assessing whether competing models of legal arguments are explained or predict facts uncovered before or during the trial process. Those models that are more heavily disconfirmed by the facts are given lower weight, as model plausibility measures, in the Bayesian model comparison and averaging framework adopted. In this way a plurality of arguments is allowed yet a single judgement based on all arguments is possible and rational.
PubDate: 2019-03-27

• Proof beyond a context-relevant doubt. A structural analysis of the
standard of proof in criminal adjudication
• Abstract: The present article proceeds from the mainstream view that the conceptual framework underpinning adversarial systems of criminal adjudication, i.e. a mixture of common-sense philosophy and probabilistic analysis, is unsustainable. In order to provide fact-finders with an operable structure of justification, we need to turn to epistemology once again. The article proceeds in three parts. First, I examine the structural features of justification and how various theories have attempted to overcome Agrippa’s trilemma. Second, I put Inferential Contextualism to the test and show that a defeasible structure of justification allocating epistemic rights and duties to all participants of an inquiry manages to dissolve the problem of scepticism. Third, I show that our epistemic practice already embodies a contextualist mechanism. Our problem was not that our Standard of Proof is inoperable but that it was not adequately conceptualized. Contextualism provides the framework to articulate the abovementioned practice and to treat ‘reasonable doubts’ as a mechanism which we can now describe in detail. The seemingly insurmountable problem with our efforts to define the concept “reasonable doubts” was the fact that we have been conflating the surface features of this mechanism and its internal structure, i.e. the rules for its use.
PubDate: 2019-03-18

• When expert opinion evidence goes wrong
• Abstract: This paper combines three computational argumentation systems to model the sequence of argumentation in a famous murder trial and the appeal procedure that followed. The paper shows how the argumentation scheme for argument from expert opinion can be built into a testing procedure whereby an argument graph is used to interpret, analyze and evaluate evidence-based natural language argumentation of the kind found in a trial. It is shown how a computational argumentation system can do this by combining argument schemes with argumentation graphs. Frighteningly, it is also shown by this example that when there are potentially confusing conflicting arguments from expert opinion, a jury can only too easily accept a conclusion prematurely before considering critical questions that need to be asked.
PubDate: 2019-03-16

• Reasoning with dimensions and magnitudes
• Abstract: This paper shows how two models of precedential constraint can be broadened to include legal information represented through dimensions. I begin by describing a standard representation of legal cases based on boolean factors alone, and then reviewing two models of constraint developed within this standard setting. The first is the “result model”, supporting only a fortiori reasoning. The second is the “reason model”, supporting a richer notion of constraint, since it allows the reasons behind a court’s decisions to be taken into account. I then show how the initial representation can be modified to incorporate dimensional information and how the result and reason models can be adapted to this new dimensional setting. As it turns out, these two models of constraint, which are distinct in the standard setting, coincide once they are transposed to the new dimensional setting, yielding exactly the same patterns of constraint. I therefore explore two ways of refining the reason model of constraint so that, even in the dimensional setting, it can still be separated from the result model.
PubDate: 2019-03-11

• Arguing about causes in law: a semi-formal framework for causal arguments
• Abstract: Disputes over causes play a central role in legal argumentation and liability attribution. Legal approaches to causation often struggle to capture cause-in-fact in complex situations, e.g. overdetermination, preemption, omission. In this paper, we first assess three current theories of causation (but-for, NESS, ‘actual causation’) to illustrate their strengths and weaknesses in capturing cause-in-fact. Secondly, we introduce a semi-formal framework for modelling causal arguments through strict and defeasible rules. Thirdly, the framework is applied to the Althen vaccine injury case. And lastly, we discuss the need for new criteria based on a common causal argumentation framework and propose ideas on how to integrate the current theories of causation to assess the strength of causal arguments, while also acknowledging the tension between evidence-based and policy-based causal analysis in law.
PubDate: 2019-03-05

• A system of communication rules for justifying and explaining beliefs
• Abstract: This paper addresses the problems of justifying and explaining beliefs about facts in the context of civil trials. The first section contains some remarks about the nature of adjudicative fact-finding and highlights the communicative features of deciding about facts in judicial context. In Sect. 2, some difficulties and the incompleteness presented by Bayesian and coherentist frameworks, which are taken as methods suitable to solve the above-mentioned problems, are pointed out. In the third section, the purely epistemic approach to the justification and the explanation of beliefs about facts is abandoned and focus is given to the dialectical nature of civil procedure, where the parties and, particularly, the judge have to make their reasoning clear enough to allow a fruitful and efficient debate about facts. For this purpose, a communication/argumentation system is put forward, consisting of fourteen intertwined rules of discourse. The system embodies the fundamental epistemic principle according to which belief is updated given new evidence, is tailored for abductive inferences and is structured on fundamental concepts of civil procedural law. The fourth section presents an empirical application of the system to a real case.
PubDate: 2019-03-05

• Building a corpus of legal argumentation in Japanese judgement documents:
towards structure-based summarisation
• Abstract: We present an annotation scheme describing the argument structure of judgement documents, a central construct in Japanese law. To support the final goal of this work, namely summarisation aimed at the legal professions, we have designed blueprint models of summaries of various granularities, and our annotation model in turn is fitted around the information needed for the summaries. In this paper we report results of a manual annotation study, showing that the annotation is stable. The annotated corpus we created contains 89 documents (37,673 sentences; 2,528,604 characters). We also designed and implemented the first two stages of an algorithm for the automatic extraction of argument structure, and present evaluation results.
PubDate: 2019-02-15

• CLAUDETTE: an automated detector of potentially unfair clauses in online
• Abstract: Terms of service of on-line platforms too often contain clauses that are potentially unfair to the consumer. We present an experimental study where machine learning is employed to automatically detect such potentially unfair clauses. Results show that the proposed system could provide a valuable tool for lawyers and consumers alike.
PubDate: 2019-02-15

• Vertical precedents in formal models of precedential constraint
• Abstract: The standard model of precedential constraint holds that a court is equally free to modify a precedent of its own and a precedent of a superior court—overruling aside, it does not differentiate horizontal and vertical precedents. This paper shows that no model can capture the U.S. doctrine of precedent without making that distinction. A precise model is then developed that does just that. This requires situating precedent cases in a formal representation of a hierarchical legal structure, and adjusting the constraint that a precedent imposes based on the relationship of the precedent court and the instant court. The paper closes with suggestions for further improvements of the model.
PubDate: 2019-02-08

• Semi-automatic knowledge population in a legal document management system
• Abstract: Every organization has to deal with operational risks, arising from the execution of a company’s primary business functions. In this paper, we describe a legal knowledge management system which helps users understand the meaning of legislative text and the relationship between norms. While much of the knowledge requires the input of legal experts, we focus in this article on NLP applications that semi-automate essential time-consuming and lower-skill tasks—classifying legal documents, identifying cross-references and legislative amendments, linking legal terms to the most relevant definitions, and extracting key elements of legal provisions to facilitate clarity and advanced search options. The use of Natural Language Processing tools to semi-automate such tasks makes the proposal a realistic commercial prospect as it helps keep costs down while allowing greater coverage.
PubDate: 2018-12-13

• Deep learning in law: early adaptation and legal word embeddings trained
on large corpora
• Abstract: Deep Learning has been widely used for tackling challenging natural language processing tasks over the recent years. Similarly, the application of Deep Neural Networks in legal analytics has increased significantly. In this survey, we study the early adaptation of Deep Learning in legal analytics focusing on three main fields; text classification, information extraction, and information retrieval. We focus on the semantic feature representations, a key instrument for the successful application of deep learning in natural language processing. Additionally, we share pre-trained legal word embeddings using the word2vec model over large corpora, comprised legislations from UK, EU, Canada, Australia, USA, and Japan among others.
PubDate: 2018-12-11

• Unsupervised and supervised text similarity systems for automated
identification of national implementing measures of European directives
• Abstract: The automated identification of national implementations (NIMs) of European directives by text similarity techniques has shown promising preliminary results. Previous works have proposed and utilized unsupervised lexical and semantic similarity techniques based on vector space models, latent semantic analysis and topic models. However, these techniques were evaluated on a small multilingual corpus of directives and NIMs. In this paper, we utilize word and paragraph embedding models learned by shallow neural networks from a multilingual legal corpus of European directives and national legislation (from Ireland, Luxembourg and Italy) to develop unsupervised semantic similarity systems to identify transpositions. We evaluate these models and compare their results with the previous unsupervised methods on a multilingual test corpus of 43 Directives and their corresponding NIMs. We also develop supervised machine learning models to identify transpositions and compare their performance with different feature sets.
PubDate: 2018-10-26

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