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  1. Automated legal reasoning with discretion to act using s(LAW).Joaquín Arias, Mar Moreno-Rebato, Jose A. Rodriguez-García & Sascha Ossowski - forthcoming - Artificial Intelligence and Law:1-24.
    Automated legal reasoning and its application in smart contracts and automated decisions are increasingly attracting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable terms the advice given. Logic Programming, specially Answer Set Programming, has a rich semantics and has been used to very concisely express complex knowledge. However, modelling discretionality to act and other vague concepts such as ambiguity cannot be expressed in top-down execution models based on Prolog, and in (...)
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  • Una clasificación de la inteligencia artificial jurídica desde la perspectiva de la filosofía del derecho.Jorge Crego (ed.) - 2023 - Coimbra: Almedina.
    Hace ya más de 40 años desde que se comenzase a explorar las posibilidades de emplear inteligencia artificial en el campo del derecho. Existen numerosos usos y propuestas en el marco de la IA y el derecho, desde la automatización de la búsqueda de la información jurídica hasta la personalización del derecho. Estas propuestas plantean diversas cuestiones relacionadas con la filosofía del derecho. La proliferación de propuestas, en ocasiones, dificulta identificar la relación existente entre cada una de ellas con debates (...)
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  • Thirty years of artificial intelligence and law: the third decade.Serena Villata, Michal Araszkiewicz, Kevin Ashley, Trevor Bench-Capon, L. Karl Branting, Jack G. Conrad & Adam Wyner - 2022 - Artificial Intelligence and Law 30 (4):561-591.
    The first issue of Artificial Intelligence and Law journal was published in 1992. This paper offers some commentaries on papers drawn from the Journal’s third decade. They indicate a major shift within Artificial Intelligence, both generally and in AI and Law: away from symbolic techniques to those based on Machine Learning approaches, especially those based on Natural Language texts rather than feature sets. Eight papers are discussed: two concern the management and use of documents available on the World Wide Web, (...)
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  • Deep Learning-Based Intelligent Robot in Sentencing.Xuan Chen - 2022 - Frontiers in Psychology 13.
    This work aims to explore the application of deep learning-based artificial intelligence technology in sentencing, to promote the reform and innovation of the judicial system. First, the concept and the principles of sentencing are introduced, and the deep learning model of intelligent robot in trials is proposed. According to related concepts, the issues that need to be solved in artificial intelligence sentencing based on deep learning are introduced. The deep learning model is integrated into the intelligent robot system, to assist (...)
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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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  • 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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  • Legal requirements on explainability in machine learning.Adrien Bibal, Michael Lognoul, Alexandre de Streel & Benoît Frénay - 2020 - Artificial Intelligence and Law 29 (2):149-169.
    Deep learning and other black-box models are becoming more and more popular today. Despite their high performance, they may not be accepted ethically or legally because of their lack of explainability. This paper presents the increasing number of legal requirements on machine learning model interpretability and explainability in the context of private and public decision making. It then explains how those legal requirements can be implemented into machine-learning models and concludes with a call for more inter-disciplinary research on explainability.
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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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  • Automatic semantic edge labeling over legal citation graphs.Ali Sadeghian, Laksshman Sundaram, Daisy Zhe Wang, William F. Hamilton, Karl Branting & Craig Pfeifer - 2018 - Artificial Intelligence and Law 26 (2):127-144.
    A large number of cross-references to various bodies of text are used in legal texts, each serving a different purpose. It is often necessary for authorities and companies to look into certain types of these citations. Yet, there is a lack of automatic tools to aid in this process. Recently, citation graphs have been used to improve the intelligibility of complex rule frameworks. We propose an algorithm that builds the citation graph from a document and automatically labels each edge according (...)
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