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  1. Thirty years of Artificial Intelligence and Law: overviews.Michał Araszkiewicz, Trevor Bench-Capon, Enrico Francesconi, Marc Lauritsen & Antonino Rotolo - 2022 - Artificial Intelligence and Law 30 (4):593-610.
    The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper discusses several topics that relate more naturally to groups of papers than a single paper published in the journal: ontologies, reasoning about evidence, the various contributions of Douglas Walton, and the practical application of the techniques of AI and Law.
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  • Argumentation schemes in AI and Law.Katie Atkinson & Trevor Bench-Capon - 2021 - Argument and Computation 12 (3):417-434.
    In this paper we describe the impact that Walton’s conception of argumentation schemes had on AI and Law research. We will discuss developments in argumentation in AI and Law before Walton’s schemes became known in that community, and the issues that were current in that work. We will then show how Walton’s schemes provided a means of addressing all of those issues, and so supplied a unifying perspective from which to view argumentation in AI and Law.
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  • Dialogue Types, Argumentation Schemes, and Mathematical Practice: Douglas Walton and Mathematics.Andrew Aberdein - 2021 - Journal of Applied Logics 8 (1):159-182.
    Douglas Walton’s multitudinous contributions to the study of argumentation seldom, if ever, directly engage with argumentation in mathematics. Nonetheless, several of the innovations with which he is most closely associated lend themselves to improving our understanding of mathematical arguments. I concentrate on two such innovations: dialogue types (§1) and argumentation schemes (§2). I argue that both devices are much more applicable to mathematical reasoning than may be commonly supposed.
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  • Vertical precedents in formal models of precedential constraint.Gabriel L. Broughton - 2019 - Artificial Intelligence and Law 27 (3):253-307.
    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 (...)
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  • Thirty years of Artificial Intelligence and Law: the first decade. [REVIEW]Guido Governatori, Trevor Bench-Capon, Bart Verheij, Michał Araszkiewicz, Enrico Francesconi & Matthias Grabmair - 2022 - Artificial Intelligence and Law 30 (4):481-519.
    The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper provides commentaries on landmark papers from the first decade of that journal. The topics discussed include reasoning with cases, argumentation, normative reasoning, dialogue, representing legal knowledge and neural networks.
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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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  • In memoriam Douglas N. Walton: the influence of Doug Walton on AI and law.Katie Atkinson, Trevor Bench-Capon, Floris Bex, Thomas F. Gordon, Henry Prakken, Giovanni Sartor & Bart Verheij - 2020 - Artificial Intelligence and Law 28 (3):281-326.
    Doug Walton, who died in January 2020, was a prolific author whose work in informal logic and argumentation had a profound influence on Artificial Intelligence, including Artificial Intelligence and Law. He was also very interested in interdisciplinary work, and a frequent and generous collaborator. In this paper seven leading researchers in AI and Law, all past programme chairs of the International Conference on AI and Law who have worked with him, describe his influence on their work.
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  • 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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  • Noise induced hearing loss: Building an application using the ANGELIC methodology.Latifa Al-Abdulkarim, Katie Atkinson, Trevor Bench-Capon, Stuart Whittle, Rob Williams & Catriona Wolfenden - 2018 - Argument and Computation 10 (1):5-22.
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  • Explainable AI tools for legal reasoning about cases: A study on the European Court of Human Rights.Joe Collenette, Katie Atkinson & Trevor Bench-Capon - 2023 - Artificial Intelligence 317 (C):103861.
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  • A top-level model of case-based argumentation for explanation: Formalisation and experiments.Henry Prakken & Rosa Ratsma - 2022 - Argument and Computation 13 (2):159-194.
    This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the (...)
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  • Eveline T. Feteris: Fundamentals of legal argumentation: Springer, 2017, 2nd edn, pp. 363.T. J. M. Bench-Capon - 2018 - Artificial Intelligence and Law 26 (3):307-314.
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  • Research in progress: report on the ICAIL 2017 doctoral consortium.Maria Dymitruk, Réka Markovich, Rūta Liepiņa, Mirna El Ghosh, Robert van Doesburg, Guido Governatori & Bart Verheij - 2018 - Artificial Intelligence and Law 26 (1):49-97.
    This paper arose out of the 2017 international conference on AI and law doctoral consortium. There were five students who presented their Ph.D. work, and each of them has contributed a section to this paper. The paper offers a view of what topics are currently engaging students, and shows the diversity of their interests and influences.
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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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  • Before and after Dung: Argumentation in AI and Law.T. J. M. Bench-Capon - 2020 - Argument and Computation 11 (1-2):221-238.
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  • How to justify a backing’s eligibility for a warrant: the justification of a legal interpretation in a hard case.Shiyang Yu & Xi Chen - 2023 - Artificial Intelligence and Law 31 (2):239-268.
    The Toulmin model has been proved useful in law and argumentation theory. This model describes the basic process in justifying a claim, which comprises six elements, i.e., claim (C), data (D), warrant (W), backing (B), qualifier (Q), and rebuttal (R). Specifically, in justifying a claim, one must put forward ‘data’ and a ‘warrant’, whereas the latter is authorized by ‘backing’. The force of the ‘claim’ being justified is represented by the ‘qualifier’, and the condition under which the claim cannot be (...)
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  • M-LAMAC: a model for linguistic assessment of mitigating and aggravating circumstances of criminal responsibility using computing with words.Carlos Rafael Rodríguez Rodríguez, Yarina Amoroso Fernández, Denis Sergeevich Zuev, Marieta Peña Abreu & Yeleny Zulueta Veliz - 2024 - Artificial Intelligence and Law 32 (3):697-739.
    The general mitigating and aggravating circumstances of criminal liability are elements attached to the crime that, when they occur, affect the punishment quantum. Cuban criminal legislation provides a catalog of such circumstances and some general conditions for their application. Such norms give judges broad discretion in assessing circumstances and adjusting punishment based on the intensity of those circumstances. In the interest of broad judicial discretion, the law does not establish specific ways for measuring circumstances’ intensity. This gives judges more freedom (...)
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