14 found
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  1.  83
    Automatically Classifying Case Texts and Predicting Outcomes.Kevin D. Ashley & Stefanie Brüninghaus - 2009 - Artificial Intelligence and Law 17 (2):125-165.
    Work on a computer program called SMILE + IBP (SMart Index Learner Plus Issue-Based Prediction) bridges case-based reasoning and extracting information from texts. The program addresses a technologically challenging task that is also very relevant from a legal viewpoint: to extract information from textual descriptions of the facts of decided cases and apply that information to predict the outcomes of new cases. The program attempts to automatically classify textual descriptions of the facts of legal problems in terms of Factors, a (...)
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  2.  49
    Case-Based Approaches to Professional Ethics: A Systematic Comparison of Students' and Ethicists' Moral Reasoning.Matthewg Keefer & Kevin D. Ashley - 2001 - Journal of Moral Education 30 (4):377-398.
    This article provides a systematic analysis of the cognitive processes required for acquiring skill in practical ethical reasoning in a professional domain. We undertook this NSF-supported research project in part to study relationships between case-based instruction in professional ethics and cognitive analyses of ethical reasoning strategies. Using a web-based experimental design, we report striking differences in the students' and ethicists' use of knowledge and reasoning. Virtually all of the ethicists and some students' protocols made significant use of specialized professional knowledge (...)
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  3.  47
    A Note on Dimensions and Factors.Edwina L. Rissland & Kevin D. Ashley - 2002 - Artificial Intelligence and Law 10 (1-3):65-77.
    In this short note, we discuss several aspectsof dimensions and the related constructof factors. We concentrate on those aspectsthat are relevant to articles in this specialissue, especially those dealing with the analysisof the wild animal cases discussed inBerman and Hafner's 1993 ICAIL article. We reviewthe basic ideas about dimensions,as used in HYPO, and point out differences withfactors, as used in subsequent systemslike CATO. Our goal is to correct certainmisconceptions that have arisen over the years.
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  4.  91
    Case-Based Reasoning and its Implications for Legal Expert Systems.Kevin D. Ashley - 1992 - Artificial Intelligence and Law 1 (2-3):113-208.
    Reasoners compare problems to prior cases to draw conclusions about a problem and guide decision making. All Case-Based Reasoning (CBR) employs some methods for generalizing from cases to support indexing and relevance assessment and evidences two basic inference methods: constraining search by tracing a solution from a past case or evaluating a case by comparing it to past cases. Across domains and tasks, however, humans reason with cases in subtly different ways evidencing different mixes of and mechanisms for these components.In (...)
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  5.  58
    Emerging AI & Law Approaches to Automating Analysis and Retrieval of Electronically Stored Information in Discovery Proceedings.Kevin D. Ashley & Will Bridewell - 2010 - Artificial Intelligence and Law 18 (4):311-320.
    This article provides an overview of, and thematic justification for, the special issue of the journal of Artificial Intelligence and Law entitled “E-Discovery”. In attempting to define a characteristic “AI & Law” approach to e-discovery, and since a central theme of AI & Law involves computationally modeling legal knowledge, reasoning and decision making, we focus on the theme of representing and reasoning with litigators’ theories or hypotheses about document relevance through a variety of techniques including machine learning. We also identify (...)
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  6.  3
    Law, Learning and Representation.Kevin D. Ashley & Edwina L. Rissland - 2003 - Artificial Intelligence 150 (1-2):17-58.
  7. An AI Model of Case-Based Legal Argument From a Jurisprudential Viewpoint.Kevin D. Ashley - 2002 - Artificial Intelligence and Law 10 (1-3):163-218.
    This article describes recent jurisprudential accountsof analogical legal reasoning andcompares them in detail to the computational modelof case-based legal argument inCATO. The jurisprudential models provide a theoryof relevance based on low-levellegal principles generated in a process ofcase-comparing reflective adjustment. Thejurisprudential critique focuses on the problemsof assigning weights to competingprinciples and dealing with erroneously decidedprecedents. CATO, a computerizedinstructional environment, employs ArtificialIntelligence techniques to teach lawstudents how to make basic legal argumentswith cases. The computational modelhelps students test legal hypotheses againsta database of (...)
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  8.  21
    Handbook of Legal Reasoning and Argumentation.Colin Aitken, Amalia Amaya, Kevin D. Ashley, Carla Bagnoli, Giorgio Bongiovanni, Bartosz Brożek, Cristiano Castelfranchi, Samuele Chilovi, Marcello Di Bello, Jaap Hage, Kenneth Einar Himma, Lewis A. Kornhauser, Emiliano Lorini, Fabrizio Macagno, Andrei Marmor, J. J. Moreso, Veronica Rodriguez-Blanco, Antonino Rotolo, Giovanni Sartor, Burkhard Schafer, Chiara Valentini, Bart Verheij, Douglas Walton & Wojciech Załuski (eds.) - 2011 - Dordrecht, Netherland: Springer Verlag.
    This handbook offers a deep analysis of the main forms of legal reasoning and argumentation from both a logical-philosophical and legal perspective. These forms are covered in an exhaustive and critical fashion, and the handbook accordingly divides in three parts: the first one introduces and discusses the basic concepts of practical reasoning. The second one discusses the main general forms of reasoning and argumentation relevant for legal discourse. The third one looks at their application in law as well as at (...)
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  9.  9
    Legal information retrieval for understanding statutory terms.Jaromír Šavelka & Kevin D. Ashley - 2022 - Artificial Intelligence and Law 30 (2):245-289.
    In this work we study, design, and evaluate computational methods to support interpretation of statutory terms. We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms. The task models the analysis of past treatment of statutory terms, an exercise lawyers routinely perform using a combination of manual and computational approaches. We treat the discovery of sentences as a special case of ad hoc document retrieval. The specifics include retrieval of short texts, specialized document types, (...)
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  10.  49
    Teaching a Process Model of Legal Argument with Hypotheticals.Kevin D. Ashley - 2009 - Artificial Intelligence and Law 17 (4):321-370.
    The research described here explores the idea of using Supreme Court oral arguments as pedagogical examples in first year classes to help students learn the role of hypothetical reasoning in law. The article presents examples of patterns of reasoning with hypotheticals in appellate legal argument and in the legal classroom and a process model of hypothetical reasoning that relates them to work in cognitive science and Artificial Intelligence. The process model describes the relationships between an advocate’s proposed test for deciding (...)
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  11.  4
    Special Issue of the Journal Artificial Intelligence on “AI & Law”.Edwina L. Rissland, Kevin D. Ashley & R. Prescott Loui - 2001 - Artificial Intelligence 129 (1-2):313-314.
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  12.  3
    Special Issue of the Journal Artificial Intelligence on “AI & Law”.Edwina L. Rissland, Kevin D. Ashley & R. Prescott Loui - 2001 - Artificial Intelligence 128 (1-2):247-248.
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  13.  2
    Special Issue of the Journal Artificial Intelligence on “AI & Law”.Edwina L. Rissland, Kevin D. Ashley & R. Prescott Loui - 2001 - Artificial Intelligence 127 (2):271-272.
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  14.  2
    Special Issue of the Journal Artificial Intelligence on “AI & Law”.Edwina L. Rissland, Kevin D. Ashley & R. Prescott Loui - 2001 - Artificial Intelligence 127 (1):165-166.
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