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  1.  2
    ICAIL Doctoral Consortium, Montreal 2019.Michał Araszkiewicz, Ilaria Angela Amantea, Saurabh Chakravarty, Robert van Doesburg, Maria Dymitruk, Marie Garin, Leilani Gilpin, Daphne Odekerken & Seyedeh Sajedeh Salehi - 2020 - Artificial Intelligence and Law 28 (2):267-280.
    This is a report on the Doctoral Consortium co-located with the 17th International Conference on Artificial Intelligence and Law in Montreal.
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  2.  9
    Taking Stock of Legal Ontologies: A Feature-Based Comparative Analysis.Valentina Leone, Luigi Di Caro & Serena Villata - 2020 - Artificial Intelligence and Law 28 (2):207-235.
    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 (...)
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  3.  2
    Taking stock of legal ontologies: a feature-based comparative analysis.Valentina Leone, Luigi Di Caro & Serena Villata - 2020 - Artificial Intelligence and Law 28 (2):207-235.
    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 (...)
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  4.  5
    Taking stock of legal ontologies: a feature-based comparative analysis.Valentina Leone, Luigi Di Caro & Serena Villata - 2020 - Artificial Intelligence and Law 28 (2):207-235.
    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 (...)
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  5.  14
    Using Machine Learning to Predict Decisions of the European Court of Human Rights.Masha Medvedeva, Michel Vols & Martijn Wieling - 2020 - Artificial Intelligence and Law 28 (2):237-266.
    When courts started publishing judgements, big data analysis 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 judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our approach highlights the potential of machine learning approaches in (...)
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  6.  3
    Using machine learning to predict decisions of the European Court of Human Rights.Masha Medvedeva, Michel Vols & Martijn Wieling - 2020 - Artificial Intelligence and Law 28 (2):237-266.
    When courts started publishing judgements, big data analysis 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 judicial decisions. With an average accuracy of 75% in predicting the violation of 9 articles of the European Convention on Human Rights our approach highlights the potential of machine learning approaches in (...)
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  7.  4
    Artificial Intelligence as Law: Presidential Address to the Seventeenth International Conference on Artificial Intelligence and Law. [REVIEW]Bart Verheij - 2020 - Artificial Intelligence and Law 28 (2):181-206.
    Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be good for us. But how to (...)
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  8.  7
    Normative decision analysis in forensic science.A. Biedermann, S. Bozza & F. Taroni - 2020 - Artificial Intelligence and Law 28 (1):7-25.
    This paper focuses on the normative analysis—in the sense of the classic decision-theoretic formulation—of decision problems that arise in connection with forensic expert reporting. We distinguish this analytical account from other common types of decision analyses, such as descriptive approaches. While decision theory is, since several decades, an extensively discussed topic in legal literature, its use in forensic science is more recent, and with an emphasis on goals such as the analysis of the logical structure of forensic expert conclusions regarding, (...)
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  9.  3
    Interactive virtue and vice in systems of arguments: a logocratic analysis. [REVIEW]Scott Brewer - 2020 - Artificial Intelligence and Law 28 (1):151-179.
    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, or that are dialectically strong, or that are rhetorically strong. 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 virtue framework to explicate the three (...)
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  10.  2
    Interactive virtue and vice in systems of arguments: a logocratic analysis. [REVIEW]Scott Brewer - 2020 - Artificial Intelligence and Law 28 (1):151-179.
    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, or that are dialectically strong, or that are rhetorically strong. 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 virtue framework to explicate the three (...)
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  11.  3
    Interactive virtue and vice in systems of arguments: a logocratic analysis. [REVIEW]Scott Brewer - 2020 - Artificial Intelligence and Law 28 (1):151-179.
    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, or that are dialectically strong, or that are rhetorically strong. 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 virtue framework to explicate the three (...)
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  12.  44
    Evidence & decision making in the law: theoretical, computational and empirical approaches.Marcello Di Bello & Bart Verheij - 2020 - Artificial Intelligence and Law 28 (1):1-5.
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  13.  11
    Group-to-individual (G2i) inferences: challenges in modeling how the U.S. court system uses brain data.Valerie Gray Hardcastle - 2020 - Artificial Intelligence and Law 28 (1):51-68.
    Regardless of formalization used, one on-going challenge for AI systems that model legal proceedings is accounting for contextual issues, particularly where judicial decisions are made in criminal cases. The law assumes a rational approach to rule application in deciding a defendant’s guilt; however, judges and juries can behave irrationally. What should a model prize: efficiency, accuracy, or fairness? Exactly whether and how to incorporate the psychology of courtroom interactions into formal models or expert systems has only just begun to be (...)
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  14.  9
    Proof beyond a context-relevant doubt. A structural analysis of the standard of proof in criminal adjudication.Kyriakos N. Kotsoglou - 2020 - Artificial Intelligence and Law 28 (1):111-133.
    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 (...)
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  15.  3
    Proof beyond a context-relevant doubt. A structural analysis of the standard of proof in criminal adjudication.Kyriakos N. Kotsoglou - 2020 - Artificial Intelligence and Law 28 (1):111-133.
    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 (...)
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  16.  4
    Proof beyond a context-relevant doubt. A structural analysis of the standard of proof in criminal adjudication.Kyriakos N. Kotsoglou - 2020 - Artificial Intelligence and Law 28 (1):111-133.
    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 (...)
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  17. Proof beyond a context-relevant doubt. A structural analysis of the standard of proof in criminal adjudication.Kyriakos N. Kotsoglou - 2020 - Artificial Intelligence and Law 28 (1):111-133.
    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 (...)
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  18.  9
    Arguing about causes in law: a semi-formal framework for causal arguments.Rūta Liepiņa, Giovanni Sartor & Adam Wyner - 2020 - Artificial Intelligence and Law 28 (1):69-89.
    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 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 (...)
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  19.  1
    Arguing about causes in law: a semi-formal framework for causal arguments.Rūta Liepiņa, Giovanni Sartor & Adam Wyner - 2020 - Artificial Intelligence and Law 28 (1):69-89.
    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 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 (...)
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  20.  1
    Arguing about causes in law: a semi-formal framework for causal arguments.Rūta Liepiņa, Giovanni Sartor & Adam Wyner - 2020 - Artificial Intelligence and Law 28 (1):69-89.
    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 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 (...)
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  21.  12
    A system of communication rules for justifying and explaining beliefs about facts in civil trials.João Marques Martins - 2020 - Artificial Intelligence and Law 28 (1):135-150.
    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 (...)
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  22.  10
    A new use case for argumentation support tools: supporting discussions of Bayesian analyses of complex criminal cases.Henry Prakken - 2020 - Artificial Intelligence and Law 28 (1):27-49.
    In this paper a new use case for legal argumentation support tools is considered: supporting discussions about analyses of complex criminal cases with the help of Bayesian probability theory. By way of a case study, two actual discussions between experts in court cases are analysed on their argumentation structure. In this study the usefulness of several recognised argument schemes is confirmed, a new argument scheme for arguments from statistics are proposed, and an analysis is given of debates between experts about (...)
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  23.  6
    Assessment criteria or standards of proof? An effort in clarification.Giovanni Tuzet - 2020 - Artificial Intelligence and Law 28 (1):91-109.
    The paper provides a conceptual distinction between evidence assessment criteria and standards of proof. Evidence must be assessed in order to check whether it satisfies a relevant standard of proof, and the assessment is operated with some criterion; so both criteria and standards are necessary for fact-finding. In addition to this conceptual point, the article addresses three main questions: Why do some scholars and decision-makers take assessment criteria as standards of proof and vice versa? Why do systems differ as to (...)
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