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  1. Reasoning with dimensions and magnitudes.John Horty - 2019 - Artificial Intelligence and Law 27 (3):309-345.
    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 (...)
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  • Representing dimensions within the reason model of precedent.Adam Rigoni - 2018 - Artificial Intelligence and Law 26 (1):1-22.
    This paper gives an account of dimensions in the reason model found in Horty : 1–33, 2011), Horty and Bench-Capon and Rigoni :133–160, 2015. doi: 10.1007/s10506-015-9166-x). The account is constructed with the purpose of rectifying problems with the approach to incorporating dimensions in Horty, namely, the problems arising from the collapse of the distinction between the reason model and the result model on that approach. Examination of the newly constructed theory revealed that the importance of dimensions in the reason model (...)
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  • HYPO's legacy: introduction to the virtual special issue.T. J. M. Bench-Capon - 2017 - Artificial Intelligence and Law 25 (2):205-250.
    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 (...)
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  • Data-centric and logic-based models for automated legal problem solving.L. Karl Branting - 2017 - Artificial Intelligence and Law 25 (1):5-27.
    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 (...)
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  • Introduction to structured argumentation.Philippe Besnard, Alejandro Garcia, Anthony Hunter, Sanjay Modgil, Henry Prakken, Guillermo Simari & Francesca Toni - 2014 - Argument and Computation 5 (1):1-4.
    In abstract argumentation, each argument is regarded as atomic. There is no internal structure to an argument. Also, there is no specification of what is an argument or an attack. They are assumed to be given. This abstract perspective provides many advantages for studying the nature of argumentation, but it does not cover all our needs for understanding argumentation or for building tools for supporting or undertaking argumentation. If we want a more detailed formalization of arguments than is available with (...)
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  • The ASPIC+ framework for structured argumentation: a tutorial.Sanjay Modgil & Henry Prakken - 2014 - Argument and Computation 5 (1):31-62.
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  • A model of legal reasoning with cases incorporating theories and values.Trevor Bench-Capon & Giovanni Sartor - 2003 - Artificial Intelligence 150 (1-2):97-143.
    Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, (...)
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  • Rules and reasons in the theory of precedent.John F. Horty - 2011 - Legal Theory 17 (1):1-33.
    The doctrine of precedent, as it has evolved within the common law, has at its heart a form of reasoning—broadly speaking, alogic—according to which the decisions of earlier courts in particular cases somehow generalize to constrain the decisions of later courts facing different cases, while still allowing these later courts a degree of freedom in responding to fresh circumstances. Although the techniques for arguing on the basis of precedent are taught early on in law schools, mastered with relative ease, and (...)
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  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Cynthia Rudin - 2019 - Nature Machine Intelligence 1.
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  • A formal analysis of some factor- and precedent-based accounts of precedential constraint.Henry Prakken - 2021 - Artificial Intelligence and Law 29 (4):559-585.
    In this paper several recent factor- and dimension-based models of precedential constraint are formally investigated and an alternative dimension-based model is proposed. Simple factor- and dimension-based syntactic criteria are identified for checking whether a decision in a new case is forced, in terms of the relevant differences between a precedent and a new case, and the difference between absence of factors and negated factors in factor-based models is investigated. Then Horty’s and Rigoni’s recent dimension-based models of precedential constraint are critically (...)
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  • Explanation in artificial intelligence: Insights from the social sciences.Tim Miller - 2019 - Artificial Intelligence 267 (C):1-38.
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  • A Survey of Methods for Explaining Black Box Models.Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Franco Turini, Fosca Giannotti & Dino Pedreschi - 2019 - ACM Computing Surveys 51 (5):1-42.
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  • On the acceptability of arguments and its fundamental role in nonmonotonic reasoning, logic programming and n-person games.Phan Minh Dung - 1995 - Artificial Intelligence 77 (2):321-357.
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  • Explanation in AI and law: Past, present and future.Katie Atkinson, Trevor Bench-Capon & Danushka Bollegala - 2020 - Artificial Intelligence 289 (C):103387.
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  • Using background knowledge in case-based legal reasoning: A computational model and an intelligent learning environment.Vincent Aleven - 2003 - Artificial Intelligence 150 (1-2):183-237.
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  • Peeking inside the black-box: A survey on explainable artificial intelligence (XAI).A. Adadi & M. Berrada - 2018 - IEEE Access 6.
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