Artificial Intelligence and Law最新文献_第3页

Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model 利用大规模预训练语言模型构建法律题库，将法律知识带给大众

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-07-06 DOI: 10.1007/s10506-023-09367-6

Mingruo Yuan, Ben Kao, Tien-Hsuan Wu, Michael M. K. Cheung, Henry W. H. Chan, Anne S. Y. Cheung, Felix W. H. Chan, Yongxi Chen

{"title":"Bringing legal knowledge to the public by constructing a legal question bank using large-scale pre-trained language model","authors":"Mingruo Yuan, Ben Kao, Tien-Hsuan Wu, Michael M. K. Cheung, Henry W. H. Chan, Anne S. Y. Cheung, Felix W. H. Chan, Yongxi Chen","doi":"10.1007/s10506-023-09367-6","DOIUrl":"10.1007/s10506-023-09367-6","url":null,"abstract":"<div><p>Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson’s terms. Second, we construct a <i>Legal Question Bank</i>, which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive <i>CLIC Recommender</i>. Given a user’s verbal description of a legal situation that requires a legal solution, CRec interprets the user’s input and shortlists questions from the question bank that are most likely relevant to the given legal situation and recommends their corresponding CLIC pages where relevant legal knowledge can be found. In this paper we focus on the technical aspects of creating an LQB. We show how large-scale pre-trained language models, such as GPT-3, can be used to generate legal questions. We compare machine-generated questions against human-composed questions and find that MGQs are more scalable, cost-effective, and more diversified, while HCQs are more precise. We also show a prototype of CRec and illustrate through an example how our 3-step approach effectively brings relevant legal knowledge to the public.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"769 - 805"},"PeriodicalIF":3.1,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42058228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

M-LAMAC: a model for linguistic assessment of mitigating and aggravating circumstances of criminal responsibility using computing with words M-LAMAC:一个使用词计算的刑事责任减轻和加重情况的语言评估模型

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-07-04 DOI: 10.1007/s10506-023-09365-8

Carlos Rafael Rodríguez Rodríguez, Yarina Amoroso Fernández, Denis Sergeevich Zuev, Marieta Peña Abreu, Yeleny Zulueta Veliz

{"title":"M-LAMAC: a model for linguistic assessment of mitigating and aggravating circumstances of criminal responsibility using computing with words","authors":"Carlos Rafael Rodríguez Rodríguez, Yarina Amoroso Fernández, Denis Sergeevich Zuev, Marieta Peña Abreu, Yeleny Zulueta Veliz","doi":"10.1007/s10506-023-09365-8","DOIUrl":"10.1007/s10506-023-09365-8","url":null,"abstract":"<div><p>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 and autonomy, but it also imposes on them more social responsibility and challenges them to manage the uncertainty and subjectivity inherent in this complex activity. This paper proposes a model to aid the linguistic assessment of circumstances’ intensity and to provide linguistic and numerical recommendations to determine an appropriate punishment interval. M-LAMAC determines the collective evaluation of circumstances of the same type, determines the prevalence of a type of circumstance by means of a compensation function, recommends the required modification in the input interval, and finally recommends a numerical interval adjusted to the judges’ initially expressed preferences. The model’s applicability is demonstrated by means of several experiments on a fictitious case of bank document forgery.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"697 - 739"},"PeriodicalIF":3.1,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48842789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrating text mining and system dynamics to evaluate financial risks of construction contracts 结合文本挖掘和系统动力学评估建筑合同财务风险

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-07-04 DOI: 10.1007/s10506-023-09366-7

Mahdi Bakhshayesh, Hamidreza Abbasianjahromi

{"title":"Integrating text mining and system dynamics to evaluate financial risks of construction contracts","authors":"Mahdi Bakhshayesh, Hamidreza Abbasianjahromi","doi":"10.1007/s10506-023-09366-7","DOIUrl":"10.1007/s10506-023-09366-7","url":null,"abstract":"<div><p>Financial risks are among the most important risks in the construction industry projects, which significantly impact project objectives, including project cost. Besides, financial risks have many interactions with each other and project parameters, which must be taken into account to analyze risks correctly. In addition, a source of financial risks in a project is the contract, which is the most important project document. Identifying terms related to financial risks in a contract and considering their effects on the risk management process is an essential issue that has been neglected. Hence, an integrated model for evaluating financial risks and their related contractual clauses were presented. To this end, the effect of financial risks on the project cost was simulated using a system dynamics model. Moreover, terms related to financial risks in a contract text were identified and extracted using text mining, and their effect was included in the system dynamics model. The model was implemented in a hospital construction project in Tehran as a case study, and its results were analyzed. The innovation of the research is integrating text mining and the system dynamics model to investigate the effect of financial risks and related contractual clauses on the project cost.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"741 - 768"},"PeriodicalIF":3.1,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47208726","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A RDF-based graph to representing and searching parts of legal documents 一种基于rdf的图形，用于表示和搜索法律文件的各个部分

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-07-01 DOI: 10.1007/s10506-023-09364-9

Francisco de Oliveira, Jose Maria Parente de Oliveira

{"title":"A RDF-based graph to representing and searching parts of legal documents","authors":"Francisco de Oliveira, Jose Maria Parente de Oliveira","doi":"10.1007/s10506-023-09364-9","DOIUrl":"10.1007/s10506-023-09364-9","url":null,"abstract":"<div><p>Despite the public availability of legal documents, there is a need for finding specific information contained in them, such as paragraphs, clauses, items and so on. With such support, users could find more specific information than only finding whole legal documents. Some research efforts have been made in this area, but there is still a lot to be done to have legal information available more easily to be found. Thus, due to the large number of published legal documents and the high degree of connectivity, simple access to the document is not enough. It is necessary to recover the related legal framework for a specific need. In other words, the retrieval of the set of legal documents and their parts related to a specific subject is necessary. Therefore, in this work, we present a proposal of a RDF-based graph to represent and search parts of legal documents, as the output of a set of terms that represents the pursued legal information. Such a proposal is well-grounded on an ontological view, which makes possible to describe the general structure of a legal system and the structure of legal documents, providing this way the grounds for the implementation of the proposed RDF graph in terms of the meaning of their parts and relationships. We posed several queries to retrieve parts of legal documents related to sets of words and the results were significant.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"667 - 695"},"PeriodicalIF":3.1,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42956866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting citations in Dutch case law with natural language processing 用自然语言处理预测荷兰判例法中的引文

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-06-28 DOI: 10.1007/s10506-023-09368-5

Iris Schepers, Masha Medvedeva, Michelle Bruijn, Martijn Wieling, Michel Vols

{"title":"Predicting citations in Dutch case law with natural language processing","authors":"Iris Schepers, Masha Medvedeva, Michelle Bruijn, Martijn Wieling, Michel Vols","doi":"10.1007/s10506-023-09368-5","DOIUrl":"10.1007/s10506-023-09368-5","url":null,"abstract":"<div><p>With the ever-growing accessibility of case law online, it has become challenging to manually identify case law relevant to one’s legal issue. In the Netherlands, the planned increase in the online publication of case law is expected to exacerbate this challenge. In this paper, we tried to predict whether court decisions are cited by other courts or not after being published, thus in a way distinguishing between more and less authoritative cases. This type of system may be used to process the large amounts of available data by filtering out large quantities of non-authoritative decisions, thus helping legal practitioners and scholars to find relevant decisions more easily, and drastically reducing the time spent on preparation and analysis. For the Dutch Supreme Court, the match between our prediction and the actual data was relatively strong (with a Matthews Correlation Coefficient of 0.60). Our results were less successful for the Council of State and the district courts (MCC scores of 0.26 and 0.17, relatively). We also attempted to identify the most informative characteristics of a decision. We found that a completely explainable model, consisting only of handcrafted metadata features, performs almost as well as a less well-explainable system based on all text of the decision.\u0000</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"807 - 837"},"PeriodicalIF":3.1,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11291598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47866539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

I beg to differ: how disagreement is handled in the annotation of legal machine learning data sets 我不同意：在法律机器学习数据集的注释中如何处理分歧

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-06-27 DOI: 10.1007/s10506-023-09369-4

Daniel Braun

{"title":"I beg to differ: how disagreement is handled in the annotation of legal machine learning data sets","authors":"Daniel Braun","doi":"10.1007/s10506-023-09369-4","DOIUrl":"10.1007/s10506-023-09369-4","url":null,"abstract":"<div><p>Legal documents, like contracts or laws, are subject to interpretation. Different people can have different interpretations of the very same document. Large parts of judicial branches all over the world are concerned with settling disagreements that arise, in part, from these different interpretations. In this context, it only seems natural that during the annotation of legal machine learning data sets, disagreement, how to report it, and how to handle it should play an important role. This article presents an analysis of the current state-of-the-art in the annotation of legal machine learning data sets. The results of the analysis show that all of the analysed data sets remove all traces of disagreement, instead of trying to utilise the information that might be contained in conflicting annotations. Additionally, the publications introducing the data sets often do provide little information about the process that derives the “gold standard” from the initial annotations, often making it difficult to judge the reliability of the annotation process. Based on the state-of-the-art, the article provides easily implementable suggestions on how to improve the handling and reporting of disagreement in the annotation of legal machine learning data sets.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"839 - 862"},"PeriodicalIF":3.1,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09369-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44532145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mining legal arguments in court decisions 挖掘法院判决中的法律论据

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-06-23 DOI: 10.1007/s10506-023-09361-y

Ivan Habernal, Daniel Faber, Nicola Recchia, Sebastian Bretthauer, Iryna Gurevych, Indra Spiecker genannt Döhmann, Christoph Burchard

{"title":"Mining legal arguments in court decisions","authors":"Ivan Habernal, Daniel Faber, Nicola Recchia, Sebastian Bretthauer, Iryna Gurevych, Indra Spiecker genannt Döhmann, Christoph Burchard","doi":"10.1007/s10506-023-09361-y","DOIUrl":"10.1007/s10506-023-09361-y","url":null,"abstract":"<div><p>Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses at https://github.com/trusthlt/mining-legal-arguments.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"1 - 38"},"PeriodicalIF":3.1,"publicationDate":"2023-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09361-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76639492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An approach to temporalised legal revision through addition of literals 通过添加文字来实现时间化法律修订

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-06-08 DOI: 10.1007/s10506-023-09363-w

Martín O. Moguillansky, Diego C. Martinez, Luciano H. Tamargo, Antonino Rotolo

{"title":"An approach to temporalised legal revision through addition of literals","authors":"Martín O. Moguillansky, Diego C. Martinez, Luciano H. Tamargo, Antonino Rotolo","doi":"10.1007/s10506-023-09363-w","DOIUrl":"10.1007/s10506-023-09363-w","url":null,"abstract":"<div><p>As lawmakers produce norms, the underlying normative system is affected showing the intrinsic dynamism of law. Through undertaken actions of legal change, the normative system is continuously modified. In a usual legislative practice, the time for an enacted legal provision to be in force may differ from that of its inclusion to the legal system, or from that in which it produces legal effects. Even more, some provisions can produce effects retroactively in time. In this article we study a simulation of such process through the formalisation of a temporalised logical framework upon which a novel belief revision model tackles the dynamic nature of law. Represented through intervals, the temporalisation of sentences allows differentiating the temporal parameters of norms. In addition, a proposed revision operator allows assessing change to the legal system by including a new temporalised literal while preserving the time-based consistency. This can be achieved either by pushing out conflictive pieces of pre-existing norms or through the modification of intervals in which such norms can be either in force, or produce effects. Finally, the construction of the temporalised revision operator is axiomatically characterised and its rational behavior proved through a corresponding representation theorem.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 3","pages":"621 - 666"},"PeriodicalIF":3.1,"publicationDate":"2023-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41942916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Encoding legislation: a methodology for enhancing technical validation, legal alignment and interdisciplinarity 编码立法:一种加强技术验证、法律一致性和跨学科性的方法

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-06-03 DOI: 10.1007/s10506-023-09350-1

Alice Witt, Anna Huggins, Guido Governatori, Joshua Buckley

{"title":"Encoding legislation: a methodology for enhancing technical validation, legal alignment and interdisciplinarity","authors":"Alice Witt, Anna Huggins, Guido Governatori, Joshua Buckley","doi":"10.1007/s10506-023-09350-1","DOIUrl":"10.1007/s10506-023-09350-1","url":null,"abstract":"<div><p>This article proposes an innovative methodology for enhancing the technical validation, legal alignment and interdisciplinarity of attempts to encode legislation. In the context of an experiment that examines how different legally trained participants convert select provisions of the Australian <i>Copyright Act </i><i>1968</i> (Cth) into machine-executable code, we find that a combination of manual and automated methods for coding validation, which focus on formal adherence to programming languages and conventions, can significantly increase the similarity of encoded rules between coders. Participants nonetheless encountered various interpretive difficulties, including syntactic ambiguity, and intra- and intertextuality, which necessitated legal evaluation, as distinct from and in addition to coding validation. Many of these difficulties can be resolved through what we call a process of ‘legal alignment’ that aims to enhance the congruence between encoded provisions and the true meaning of a statute as determined by the courts. However, some difficulties cannot be overcome in advance, such as factual indeterminacy. Given the inherently interdisciplinary nature of encoding legislation, we argue that it is desirable for ‘rules as code’ (‘RaC’) initiatives to have, at a minimum, legal subject matter, statutory interpretation and technical programming expertise. Overall, we contend that technical validation, legal alignment and interdisciplinary teamwork are integral to the success of attempts to encode legislation. While legal alignment processes will vary depending on jurisdictionally-specific principles and practices of statutory interpretation, the technical and interdisciplinary components of our methodology are transferable across regulatory contexts, bodies of law and Commonwealth and other jurisdictions.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 2","pages":"293 - 324"},"PeriodicalIF":3.1,"publicationDate":"2023-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09350-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47194421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Compliance checking on first-order knowledge with conflicting and compensatory norms: a comparison among currently available technologies 对具有冲突和补偿规范的一阶知识的合规性检查：当前可用技术之间的比较

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2023-06-02 DOI: 10.1007/s10506-023-09360-z

Livio Robaldo, Sotiris Batsakis, Roberta Calegari, Francesco Calimeri, Megumi Fujita, Guido Governatori, Maria Concetta Morelli, Francesco Pacenza, Giuseppe Pisano, Ken Satoh, Ilias Tachmazidis, Jessica Zangari

{"title":"Compliance checking on first-order knowledge with conflicting and compensatory norms: a comparison among currently available technologies","authors":"Livio Robaldo, Sotiris Batsakis, Roberta Calegari, Francesco Calimeri, Megumi Fujita, Guido Governatori, Maria Concetta Morelli, Francesco Pacenza, Giuseppe Pisano, Ken Satoh, Ilias Tachmazidis, Jessica Zangari","doi":"10.1007/s10506-023-09360-z","DOIUrl":"10.1007/s10506-023-09360-z","url":null,"abstract":"<div><p>This paper analyses and compares some of the automated reasoners that have been used in recent research for compliance checking. Although the list of the considered reasoners is not exhaustive, we believe that our analysis is representative enough to take stock of the current state of the art in the topic. We are interested here in formalizations at the <i>first-order</i> level. Past literature on normative reasoning mostly focuses on the <i>propositional</i> level. However, the propositional level is of little usefulness for concrete LegalTech applications, in which compliance checking must be enforced on (large) sets of individuals. Furthermore, we are interested in technologies that are <i>freely available</i> and that can be further investigated and compared by the scientific community. In other words, this paper does not consider technologies only employed in industry and/or whose source code is non-accessible. This paper formalizes a selected use case in the considered reasoners and compares the implementations, also in terms of simulations with respect to shared synthetic datasets. The comparison will highlight that lot of further research still needs to be done to integrate the benefits featured by the different reasoners into a single standardized first-order framework, suitable for LegalTech applications. All source codes are freely available at https://github.com/liviorobaldo/compliancecheckers, together with instructions to locally reproduce the simulations.\u0000</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"32 2","pages":"505 - 555"},"PeriodicalIF":3.1,"publicationDate":"2023-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-023-09360-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45255388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0