4,258 research outputs found

    Professor H.L.A. Hart’s Concept of Law

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    Explaining Legal Concepts with Augmented Large Language Models (GPT-4)

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    Interpreting the meaning of legal open-textured terms is a key task of legal professionals. An important source for this interpretation is how the term was applied in previous court cases. In this paper, we evaluate the performance of GPT-4 in generating factually accurate, clear and relevant explanations of terms in legislation. We compare the performance of a baseline setup, where GPT-4 is directly asked to explain a legal term, to an augmented approach, where a legal information retrieval module is used to provide relevant context to the model, in the form of sentences from case law. We found that the direct application of GPT-4 yields explanations that appear to be of very high quality on their surface. However, detailed analysis uncovered limitations in terms of the factual accuracy of the explanations. Further, we found that the augmentation leads to improved quality, and appears to eliminate the issue of hallucination, where models invent incorrect statements. These findings open the door to the building of systems that can autonomously retrieve relevant sentences from case law and condense them into a useful explanation for legal scholars, educators or practicing lawyers alike

    Information Technology and Lawyers. Advanced Technology in the Legal Domain, from Challenges to Daily Routine

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    Formal models of statutory interpretation in multilingual legal systems

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    Avoiding the Common Wisdom Fallacy: The Role of Social Sciences in Constitutional Adjudication

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    More than one hundred years ago, the U.S. Supreme Court started to refer to social science evidence in its judgments. However, this has not resonated with many constitutional courts outside the United States, in particular in continental Europe. This contribution has a twofold aim. First, it tries to show that legal reasoning in constitutional law is often based on empirical assumptions so that there is a strong need for the use of social sciences. However, constitutional courts often lack the necessary expertise to deal with empirical questions. Therefore, I will discuss three potential strategies to make use of social science evidence. Judges can interpret social facts on their own, they can afford a margin of appreciation to the legislator, or they can defer the question to social science experts. It will be argued that none of these strategies is satisfactory so that courts will have to employ a combination of different strategies. In order to illustrate the argument, I will discuss decisions of different jurisdictions, including the United States, Canada, Germany and South Africa.proportionality, comparative law, Germany, Uncertainty, margin of appreciation, constitutional law, Canada, South Africa, social sciences, empiricism

    Law and Limits: How Categories Construct Constitutional Meaning

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    Computational models of ontology evolution in legal reasoning

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    This thesis analyses the problem of creating computational models of ontology evolution in legal reasoning. Ontology evolution is the process of change that happens to a theory as it is used by agents within a domain. In the legal domain these theories are the laws that define acceptable behaviours and the meta-legal theories that govern the application of the laws. We survey the background subjects required to understand the problem and the relevant literature within AI and Law. We argue that context and commonsense are necessary features of a model of ontology evolution in legal reasoning; and propose a model of legal reasoning based upon creating a discourse context. We conclude by arguing that there is a distinction between prescriptive and descriptive models of ontology evolution; with a prescriptive model being a social and philosophical problem, rather than a technical one, and a descriptive model being an AI-complete problem
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