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Publisher: Springer-Verlag (Total: 2351 journals)

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 Artificial Intelligence and LawJournal Prestige (SJR): 0.937 Citation Impact (citeScore): 2Number of Followers: 11      Hybrid journal (It can contain Open Access articles) ISSN (Print) 1572-8382 - ISSN (Online) 0924-8463 Published by Springer-Verlag  [2351 journals]
• Proof beyond a context-relevant doubt. A structural analysis of the
standard of proof in criminal adjudication
• Abstract: 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: 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: 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: 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: 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

• Semantic types of legal norms in German laws: classification and analysis
using local linear explanations
• Abstract: Abstract This paper describes the automated classification of legal norms in German statutes with regard to their semantic type. We propose a semantic type taxonomy for norms in the German civil law domain consisting of nine different types focusing on functional aspects, such as Duties, Prohibitions, Permissions, etc. We performed four iterations in classifying legal norms with a rule-based approach using a manually labeled dataset, i.e., tenancy law, of the German Civil Code ( $$\hbox {n} = 601$$ ). During this experiment the $$F_1$$ score continuously improved from 0.52 to 0.78. In contrast, a machine learning based approach for the classification was implemented. A performance of $$F_1 = 0.83$$ was reached. Traditionally, machine learning classifiers lack of transparency with regard to their decisions. We extended our approach using so-called local linear approximations, which is a novel technique to analyze and inspect a trained classifier’s behavior. We can show that there are significant similarities of manually crafted knowledge, i.e., rules and pattern definitions, and the trained decision structures of machine learning approaches.
PubDate: 2019-03-01

• Consistent pathway analysis: a structured analytic method
• Abstract: Abstract Mistakes during criminal investigations are costly, leading to wrongful convictions, so it is helpful to employ rigorous analytic methods to help mitigate errors and biases. This paper introduces a new method to help make sense of a set of information, allowing thought processes to be externalised in a systematic and transparent manner. While this method is presented in a criminal investigation context, it can be applied to any situation where analysis of several hypotheses and evidence is required. Open source software was created and used to test the method empirically. A simulated criminal investigation was carried out by seventy-five trainee investigators, the results showed better conclusions were reached when using the method.
PubDate: 2019-03-01

• The boundaries of legal personhood: how spontaneous intelligence can
problematise differences between humans, artificial intelligence,
companies and animals
• Abstract: Abstract In this paper, we identify the way in which various forms of legal personhood can be differentiated from one another by comparing these entities with a—not too farfetched—hypothetical situation in which intelligence spontaneously evolves (i.e. without human design) within the internet: spontaneous intelligence (“SI”). In these terms, we consider the challenges that may arise where SI as an entity: has no owner, no designer, and no controller; has evolved into existence as a non-human created intelligence; is autonomous; has no physical form; and, although it exists around the world, exists in no particular jurisdiction. Based on this refined notion of SI, we consider issues related to the recognition of such an entity’s legal personhood. By briefly exploring the attribution of legal personality to various entities—including, humans, corporations, artificial intelligence (“AI”) (in various forms) and higher forms of animal life—we differentiate SI from these other forms of intelligence whilst illustrating it shares most characteristics with human intelligence and not, as may intuitively be thought, with various forms of AI. After critically evaluating the classification of these various forms of intelligence, we briefly suggest some ramifications of these differences and suggest that the approach adopted may assist in drawing more effective boundaries between the entities that are already recognised as legal persons, as well as between sub-categories of entities, such as various forms of AI.
PubDate: 2019-03-01

• Maintainable process model driven online legal expert systems
• Abstract: Abstract Legal expert systems are computer applications that can mimic the consultation process of a legal expert to provide advice specific to a given scenario. The core of these systems is the experts’ knowledge captured in a sophisticated and often complex logic or rule base. Such complex systems rely on both knowledge engineers or system programmers and domain experts to maintain and update in response to changes in law or circumstances. This paper describes a pragmatic approach using process modelling techniques that enables a complex legal expert system to be maintained and updated dynamically by a domain expert such as a legal practitioner with little computing knowledge. The approach is illustrated using a case study on the design of an online expert system that allows a user to navigate through complex legal options in the domain of International Family Law.
PubDate: 2019-03-01

• Judicial analytics and the great transformation of American Law
• Abstract: Abstract Predictive judicial analytics holds the promise of increasing efficiency and fairness of law. Judicial analytics can assess extra-legal factors that influence decisions. Behavioral anomalies in judicial decision-making offer an intuitive understanding of feature relevance, which can then be used for debiasing the law. A conceptual distinction between inter-judge disparities in predictions and inter-judge disparities in prediction accuracy suggests another normatively relevant criterion with regards to fairness. Predictive analytics can also be used in the first step of causal inference, where the features employed in the first step are exogenous to the case. Machine learning thus offers an approach to assess bias in the law and evaluate theories about the potential consequences of legal change.
PubDate: 2019-03-01

• Building a corpus of legal argumentation in Japanese judgement documents:
towards structure-based summarisation
• Abstract: 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: 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: 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

• An axiomatic characterization of temporalised belief revision in the law
• Abstract: Abstract This paper presents a belief revision operator that considers time intervals for modelling norm change in the law. This approach relates techniques from belief revision formalisms and time intervals with temporalised rules for legal systems. Our goal is to formalise a temporalised belief base and corresponding timed derivation, together with a proper revision operator. This operator may remove rules when needed or adapt intervals of time when contradictory norms are added in the system. For the operator, both constructive definition and an axiomatic characterisation by representation theorems are given.
PubDate: 2019-01-10

• Semi-automatic knowledge population in a legal document management system
• Abstract: 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: 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: 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

• On legal contracts, imperative and declarative smart contracts, and
blockchain systems
• Authors: Guido Governatori; Florian Idelberger; Zoran Milosevic; Regis Riveret; Giovanni Sartor; Xiwei Xu
Abstract: Abstract This paper provides an analysis of how concepts pertinent to legal contracts can influence certain aspects of their digital implementation through smart contracts, as inspired by recent developments in distributed ledger technology. We discuss how properties of imperative and declarative languages including the underlying architectures to support contract management and lifecycle apply to various aspects of legal contracts. We then address these properties in the context of several blockchain architectures. While imperative languages are commonly used to implement smart contracts, we find that declarative languages provide more natural ways to deal with certain aspects of legal contracts and their automated management.
PubDate: 2018-03-05
DOI: 10.1007/s10506-018-9223-3

• RuleRS: a rule-based architecture for decision support systems
Abstract: Abstract Decision-makers in governments, enterprises, businesses and agencies or individuals, typically, make decisions according to various regulations, guidelines and policies based on existing records stored in various databases, in particular, relational databases. To assist decision-makers, an expert system, encompasses interactive computer-based systems or subsystems to support the decision-making process. Typically, most expert systems are built on top of transaction systems, databases, and data models and restricted in decision-making to the analysis, processing and presenting data and information, and they do not provide support for the normative layer. This paper will provide a solution to one specific problem that arises from this situation, namely the lack of tool/mechanism to demonstrate how an expert system is well-suited for supporting decision-making activities drawn from existing records and relevant legal requirements aligned existing records stored in various databases.We present a Rule-based (pre and post) reporting systems (RuleRS) architecture, which is intended to integrate databases, in particular, relational databases, with a logic-based reasoner and rule engine to assist in decision-making or create reports according to legal norms. We argue that the resulting RuleRS provides an efficient and flexible solution to the problem at hand using defeasible inference. To this end, we have also conducted empirical evaluations of RuleRS performance.
PubDate: 2018-02-23
DOI: 10.1007/s10506-018-9218-0

• Narration in judiciary fact-finding: a probabilistic explication
• Authors: Rafal Urbaniak
Abstract: Abstract Legal probabilism is the view that juridical fact-finding should be modeled using Bayesian methods. One of the alternatives to it is the narration view, according to which instead we should conceptualize the process in terms of competing narrations of what (allegedly) happened. The goal of this paper is to develop a reconciliatory account, on which the narration view is construed from the Bayesian perspective within the framework of formal Bayesian epistemology.
PubDate: 2018-02-22
DOI: 10.1007/s10506-018-9219-z

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