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 Artificial Intelligence and Law   [SJR: 0.288]   [H-I: 25]   [10 followers]  Follow         Hybrid journal (It can contain Open Access articles)    ISSN (Print) 1572-8382 - ISSN (Online) 0924-8463    Published by Springer-Verlag  [2353 journals]
• Ontology-based information extraction for juridical events with case
studies in Brazilian legal realm
• Authors: Denis Andrei de Araujo; Sandro José Rigo; Jorge Luis Victória Barbosa
Abstract: Abstract The number of available legal documents has presented an enormous growth in recent years, and the digital processing of such materials is prompting the necessity of systems that support the automatic relevant information extraction. This work presents a system for ontology-based information extraction from natural language texts, able to identify a set of legal events. The system is based on an innovative methodology based on domain ontology of legal events and a set of linguistic rules, integrated through inference mechanism, resulting in a flexible approach and scalable approach. A case study with the use of documents from the Superior Court in Brazil is related, with satisfactory results in precision and recall.
PubDate: 2017-07-24
DOI: 10.1007/s10506-017-9203-z

• HYPO’S legacy: introduction to the virtual special issue
• Authors: T. J. M. Bench-Capon
Abstract: Abstract This paper is an introduction to a virtual special issue of AI and Law exploring the legacy of the influential HYPO system of Rissland and Ashley. The papers included are: Arguments and cases: An inevitable intertwining, BankXX: Supporting legal arguments through heuristic retrieval, Modelling reasoning with precedents in a formal dialogue Game, A note on dimensions and factors, An empirical investigation of reasoning with legal cases through theory construction and application, Automatically classifying case texts and predicting outcomes, A factor-based definition of precedential constraint and An improved factor based approach to precedential constraint. After describing HYPO, in this introduction to the special issue I look at various aspects of its influence on AI and Law: the developments led by Rissland at Amherst; the developments led by Ashley in Pittsburgh; the expression of these ideas in terms of rule based systems, and their subsequent formalisation; value based theories, which were inspired by a critique of HYPO; and contemporary approaches which revive the idea of dimensions.
PubDate: 2017-06-02
DOI: 10.1007/s10506-017-9201-1

• On computable numbers with an application to the AlanTuringproblem
• Authors: C. F. Huws; J. C. Finnis
Abstract: Abstract This paper explores the question of whether or not the law is a computable number in the sense described by Alan Turing in his 1937 paper ‘On computable numbers with an application to the Entscheidungsproblem.’ Drawing upon the legal, social, and political context of Alan Turing’s own involvement with the law following his arrest in 1952 for the criminal offence of gross indecency, the article explores the parameters of computability within the law and analyses the applicability of Turing’s computability thesis within the context of legal decision-making.
PubDate: 2017-05-13
DOI: 10.1007/s10506-017-9200-2

• Proof with and without probabilities
• Authors: Bart Verheij
Abstract: Abstract Evidential reasoning is hard, and errors can lead to miscarriages of justice with serious consequences. Analytic methods for the correct handling of evidence come in different styles, typically focusing on one of three tools: arguments, scenarios or probabilities. Recent research used Bayesian networks for connecting arguments, scenarios, and probabilities. Well-known issues with Bayesian networks were encountered: More numbers are needed than are available, and there is a risk of misinterpretation of the graph underlying the Bayesian network, for instance as a causal model. The formalism presented here models presumptive arguments about coherent hypotheses that are compared in terms of their strength. No choice is needed between qualitative or quantitative analytic styles, since the formalism can be interpreted with and without numbers. The formalism is applied to key concepts in argumentative, scenario and probabilistic analyses of evidential reasoning, and is illustrated with a fictional crime investigation example based on Alfred Hitchcock’s film ‘To Catch A Thief’.
PubDate: 2017-03-11
DOI: 10.1007/s10506-017-9199-4

• Recognizing cited facts and principles in legal judgements
• Authors: Olga Shulayeva; Advaith Siddharthan; Adam Wyner
Abstract: Abstract In common law jurisdictions, legal professionals cite facts and legal principles from precedent cases to support their arguments before the court for their intended outcome in a current case. This practice stems from the doctrine of stare decisis, where cases that have similar facts should receive similar decisions with respect to the principles. It is essential for legal professionals to identify such facts and principles in precedent cases, though this is a highly time intensive task. In this paper, we present studies that demonstrate that human annotators can achieve reasonable agreement on which sentences in legal judgements contain cited facts and principles (respectively, $$\kappa =0.65$$ and $$\kappa =0.95$$ for inter- and intra-annotator agreement). We further demonstrate that it is feasible to automatically annotate sentences containing such legal facts and principles in a supervised machine learning framework based on linguistic features, reporting per category precision and recall figures of between 0.79 and 0.89 for classifying sentences in legal judgements as cited facts, principles or neither using a Bayesian classifier, with an overall $$\kappa$$ of 0.72 with the human-annotated gold standard.
PubDate: 2017-03-11
DOI: 10.1007/s10506-017-9197-6

• Introduction to the special issue on Artificial Intelligence for Justice
(AI4J)
• Authors: Floris Bex; Henry Prakken; Tom van Engers; Bart Verheij
PubDate: 2017-03-09
DOI: 10.1007/s10506-017-9198-5

• Norms and value based reasoning: justifying compliance and violation
• Authors: Trevor Bench-Capon; Sanjay Modgil
Abstract: Abstract There is an increasing need for norms to be embedded in technology as the widespread deployment of applications such as autonomous driving, warfare and big data analysis for crime fighting and counter-terrorism becomes ever closer. Current approaches to norms in multi-agent systems tend either to simply make prohibited actions unavailable, or to provide a set of rules (principles) which the agent is obliged to follow, either as part of its design or to avoid sanctions and punishments. In this paper we argue for the position that agents should be equipped with the ability to reason about a system’s norms, by reasoning about the social and moral values that norms are designed to serve; that is, perform the sort of moral reasoning we expect of humans. In particular we highlight the need for such reasoning when circumstances are such that the rules should arguably be broken, so that the reasoning can guide agents in deciding whether to comply with the norms and, if violation is desirable, how best to violate them. One approach to enabling this is to make use of an argumentation scheme based on values and designed for practical reasoning: arguments for and against actions are generated using this scheme and agents choose between actions based on their preferences over these values. Moral reasoning then requires that agents have an acceptable set of values and an acceptable ordering on their values. We first discuss how this approach can be used to think about and justify norms in general, and then discuss how this reasoning can be used to think about when norms should be violated, and the form this violation should take. We illustrate how value based reasoning can be used to decide when and how to violate a norm using a road traffic example. We also briefly consider what makes an ordering on values acceptable, and how such an ordering might be determined.
PubDate: 2017-03-09
DOI: 10.1007/s10506-017-9194-9

• On the concept of relevance in legal information retrieval
• Authors: Marc van Opijnen; Cristiana Santos
Abstract: Abstract The concept of ‘relevance’ is crucial to legal information retrieval, but because of its intuitive understanding it goes undefined too easily and unexplored too often. We discuss a conceptual framework on relevance within legal information retrieval, based on a typology of relevance dimensions used within general information retrieval science, but tailored to the specific features of legal information. This framework can be used for the development and improvement of legal information retrieval systems.
PubDate: 2017-03-04
DOI: 10.1007/s10506-017-9195-8

• Reading agendas between the lines, an exercise
• Authors: Giovanni Sileno; Alexander Boer; Tom van Engers
Abstract: Abstract This work presents elements for an alternative operationalization of monitoring and diagnosis of multi-agent systems, developed in the context of compliance checking. In contrast to traditional accounts of model-based diagnosis, and most proposals concerning non-compliance, our method does not consider any commitment towards the individual unit of agency. Identity is considered to be mostly an attribute to assign responsibility, and not as the only referent to a source of intentionality. The proposed method requires as input a set of prototypical agent-roles known to be relevant for the domain, and an observation, i.e. evidence collected by a monitor agent. We elaborate on a concrete example concerning tax frauds in real-estate transactions.
PubDate: 2017-03-02
DOI: 10.1007/s10506-017-9196-7

• Data-centric and logic-based models for automated legal problem solving
• Authors: L. Karl Branting
Abstract: Abstract Logic-based approaches to legal problem solving model the rule-governed nature of legal argumentation, justification, and other legal discourse but suffer from two key obstacles: the absence of efficient, scalable techniques for creating authoritative representations of legal texts as logical expressions; and the difficulty of evaluating legal terms and concepts in terms of the language of ordinary discourse. Data-centric techniques can be used to finesse the challenges of formalizing legal rules and matching legal predicates with the language of ordinary parlance by exploiting knowledge latent in legal corpora. However, these techniques typically are opaque and unable to support the rule-governed discourse needed for persuasive argumentation and justification. This paper distinguishes representative legal tasks to which each approach appears to be particularly well suited and proposes a hybrid model that exploits the complementarity of each.
PubDate: 2017-03-02
DOI: 10.1007/s10506-017-9193-x

• Contract automata
• Authors: Shaun Azzopardi; Gordon J. Pace; Fernando Schapachnik; Gerardo Schneider
Pages: 203 - 243
Abstract: Abstract Deontic logic as a way of formally reasoning about norms, an important area in AI and law, has traditionally concerned itself about formalising provisions of general statutes. Despite the long history of deontic logic, given the wide scope of the logic, it is difficult, if not impossible, to formalise all these notions in a single formalism, and there are still ongoing debates on appropriate semantics for deontic modalities in different contexts. In this paper, we restrict our attention to contracts between interactive parties, which are both general enough to be an interesting object of study but specific enough so as to narrow down the debates regarding the meaning of modalities, and present a formalism for reasoning about them.
PubDate: 2016-09-16
DOI: 10.1007/s10506-016-9185-2
Issue No: Vol. 24, No. 3 (2016)

• Using sensitive personal data may be necessary for avoiding discrimination
in data-driven decision models
• Authors: Indrė Žliobaitė; Bart Custers
Pages: 183 - 201
Abstract: Abstract Increasing numbers of decisions about everyday life are made using algorithms. By algorithms we mean predictive models (decision rules) captured from historical data using data mining. Such models often decide prices we pay, select ads we see and news we read online, match job descriptions and candidate CVs, decide who gets a loan, who goes through an extra airport security check, or who gets released on parole. Yet growing evidence suggests that decision making by algorithms may discriminate people, even if the computing process is fair and well-intentioned. This happens due to biased or non-representative learning data in combination with inadvertent modeling procedures. From the regulatory perspective there are two tendencies in relation to this issue: (1) to ensure that data-driven decision making is not discriminatory, and (2) to restrict overall collecting and storing of private data to a necessary minimum. This paper shows that from the computing perspective these two goals are contradictory. We demonstrate empirically and theoretically with standard regression models that in order to make sure that decision models are non-discriminatory, for instance, with respect to race, the sensitive racial information needs to be used in the model building process. Of course, after the model is ready, race should not be required as an input variable for decision making. From the regulatory perspective this has an important implication: collecting sensitive personal data is necessary in order to guarantee fairness of algorithms, and law making needs to find sensible ways to allow using such data in the modeling process.
PubDate: 2016-05-07
DOI: 10.1007/s10506-016-9182-5
Issue No: Vol. 24, No. 2 (2016)

• Accommodating change
• Authors: Latifa Al-Abdulkarim; Katie Atkinson; Trevor Bench-Capon
Abstract: Abstract The third of Berman and Hafner’s early nineties papers on reasoning with legal cases concerned temporal context, in particular the evolution of case law doctrine over time in response to new cases and against a changing background of social values and purposes. In this paper we consider the ways in which changes in case law doctrine can be accommodated in a recently proposed methodology for encapsulating case law theories (the ANGELIC methodology based on Abstract Dialectical Frameworks), and relate these changes the sources of change identified by Berman and Hafner.
PubDate: 2016-11-16
DOI: 10.1007/s10506-016-9190-5

• Legal personality of robots, corporations, idols and chimpanzees: a quest
for legitimacy
• Authors: S. M. Solaiman
Abstract: Abstract Robots are now associated with various aspects of our lives. These sophisticated machines have been increasingly used in different manufacturing industries and services sectors for decades. During this time, they have been a factor in causing significant harm to humans, prompting questions of liability. Industrial robots are presently regarded as products for liability purposes. In contrast, some commentators have proposed that robots be granted legal personality, with an overarching aim of exonerating the respective creators and users of these artefacts from liability. This article is concerned mainly with industrial robots that exercise some degree of self-control as programmed, though the creation of fully autonomous robots is still a long way off. The proponents of the robot’s personality compare these machines generally with corporations, and sporadically with, inter alia, animals, and idols, in substantiating their arguments. This article discusses the attributes of legal personhood and the justifications for the separate personality of corporations and idols. It then demonstrates the reasons for refusal of an animal’s personality. It concludes that robots are ineligible to be persons, based on the requirements of personhood.
PubDate: 2016-11-14
DOI: 10.1007/s10506-016-9192-3

• Special issue in memory of Carole Hafner: editor’s introduction
• Authors: T. J. M. Bench-Capon
Abstract: Abstract In this introduction I give an overview of Carole Hafner’s work and discuss the papers in this volume. The final section offers some more personal reminiscences of Carole and her contribution to the AI and Law community, from myself and other colleagues.
PubDate: 2016-11-10
DOI: 10.1007/s10506-016-9191-4

• Special issue in memory of Carole Hafner: editor’s introduction
• Authors: T. J. M. Bench-Capon
Abstract: Abstract In this introduction I give an overview of Carole Hafner’s work and discuss the papers in this volume. The final section offers some more personal reminiscences of Carole and her contribution to the AI and Law community, from myself and other colleagues.
PubDate: 2016-11-10
DOI: 10.1007/s10506-016-9191-4

• Formalizing value-guided argumentation for ethical systems design
• Authors: Bart Verheij
Abstract: Abstract The persuasiveness of an argument depends on the values promoted and demoted by the position defended. This idea, inspired by Perelman’s work on argumentation, has become a prominent theme in artificial intelligence research on argumentation since the work by Hafner and Berman on teleological reasoning in the law, and was further developed by Bench-Capon in his value-based argumentation frameworks. One theme in the study of value-guided argumentation is the comparison of values. Formal models involving value comparison typically use either qualitative or quantitative primitives. In this paper, techniques connecting qualitative and quantitative primitives recently developed for evidential argumentation are applied to value-guided argumentation. By developing the theoretical understanding of intelligent systems guided by embedded values, the paper is a step towards ethical systems design, much needed in these days of ever more pervasive AI techniques.
PubDate: 2016-11-08
DOI: 10.1007/s10506-016-9189-y

• Formalizing value-guided argumentation for ethical systems design
• Authors: Bart Verheij
Abstract: Abstract The persuasiveness of an argument depends on the values promoted and demoted by the position defended. This idea, inspired by Perelman’s work on argumentation, has become a prominent theme in artificial intelligence research on argumentation since the work by Hafner and Berman on teleological reasoning in the law, and was further developed by Bench-Capon in his value-based argumentation frameworks. One theme in the study of value-guided argumentation is the comparison of values. Formal models involving value comparison typically use either qualitative or quantitative primitives. In this paper, techniques connecting qualitative and quantitative primitives recently developed for evidential argumentation are applied to value-guided argumentation. By developing the theoretical understanding of intelligent systems guided by embedded values, the paper is a step towards ethical systems design, much needed in these days of ever more pervasive AI techniques.
PubDate: 2016-11-08
DOI: 10.1007/s10506-016-9189-y

• Cognitive computing and proposed approaches to conceptual organization of
case law knowledge bases: a proposed model for information preparation,
indexing, and analysis
• Authors: Amie Taal; James A. Sherer; Kerri-Ann Bent; Emily R. Fedeles
Abstract: Abstract Carole Hafner’s scholarship on the conceptual organization of case law knowledge bases (COC) was an original approach to distilling a library’s worth of cases into a manageable subset that any given legal researcher could review. Her approach applied concept indexation and concept search based on an annotation model of three interacting components combined with a system of expert legal reasoning to aid in the retrieval of pertinent case law. Despite the clear value this tripartite approach would afford to researchers in search of cases with similar fact patterns and desired (or undesired) outcomes, this approach has not been applied consistently in the intervening years since its introduction. Specifically, the conceptual representation of domain concepts and the case frames were not pursued by researchers, and they were not applied by the legal case indexing services that came to dominate the electronic case law market. Advances since Hafner’s original scholarship in the form of (1) digitized case law and related materials; (2) computer science analytical protocols; and (3) more advanced forms of artificial intelligence approaches present the question of whether Hafner’s COC model could move from the hypothetical to the real.
PubDate: 2016-10-21
DOI: 10.1007/s10506-016-9188-z

• From Berman and Hafner’s teleological context to Baude and Sachs’
interpretive defaults: an ontological challenge for the next decades of AI
and Law
• Authors: Ronald P. Loui
Abstract: Abstract This paper revisits the challenge of Berman and Hafner’s “missing link” paper on representing teleological structure in case-based legal reasoning. It is noted that this was mainly an ontological challenge to represent some of what made legal reasoning distinctive, which was given less attention than factual similarity in the dominant AI and Law paradigm, deriving from HYPO. The response to their paper is noted and briefly evaluated. A parallel is drawn to a new challenge to provide deep structure to the legal context of textual meaning, drawing on the forthcoming work of two Constitutional law scholars who appear to place some faith in the ways of thinking that AI and Law has developed.
PubDate: 2016-10-13
DOI: 10.1007/s10506-016-9186-1

• Eunomos, a legal document and knowledge management system for the Web to
provide relevant, reliable and up-to-date information on the law
• Authors: Guido Boella; Luigi Di Caro; Llio Humphreys; Livio Robaldo; Piercarlo Rossi; Leendert van der Torre
Abstract: Abstract This paper describes the Eunomos software, an advanced legal document and knowledge management system, based on legislative XML and ontologies. We describe the challenges of legal research in an increasingly complex, multi-level and multi-lingual world and how the Eunomos software helps users cut through the information overload to get the legal information they need in an organized and structured way and keep track of the state of the relevant law on any given topic. Using NLP tools to semi-automate the lower-skill tasks makes this ambitious project a realistic commercial prospect as it helps keep costs down while at the same time allowing greater coverage. We describe the core system from workflow and technical perspectives, and discuss applications of the system for various user groups.
PubDate: 2016-06-28
DOI: 10.1007/s10506-016-9184-3

• A method for explaining Bayesian networks for legal evidence with
scenarios
• Authors: Charlotte S. Vlek; Henry Prakken; Silja Renooij; Bart Verheij
Abstract: Abstract In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for analysing parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study.
PubDate: 2016-06-08
DOI: 10.1007/s10506-016-9183-4

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