Artificial Intelligence and Law最新文献_第10页

Towards a machine understanding of Malawi legal text 实现对马拉维法律文本的机器理解

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-10-23 DOI: 10.1007/s10506-021-09303-6

Amelia V. Taylor, Eva Mfutso-Bengo

引用次数: 2

Contract as automaton: representing a simple financial agreement in computational form 作为自动机的合同:以计算形式表示简单的财务协议

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-10-13 DOI: 10.1007/s10506-021-09300-9

Mark D. Flood, Oliver R. Goodenough

{"title":"Contract as automaton: representing a simple financial agreement in computational form","authors":"Mark D. Flood, Oliver R. Goodenough","doi":"10.1007/s10506-021-09300-9","DOIUrl":"10.1007/s10506-021-09300-9","url":null,"abstract":"<div><p>We show that the fundamental legal structure of a well-written financial contract follows a state-transition logic that can be formalized mathematically as a finite-state machine (specifically, a deterministic finite automaton or DFA). The automaton defines the states that a financial relationship can be in, such as “default,” “delinquency,” “performing,” etc., and it defines an “alphabet” of events that can trigger state transitions, such as “payment arrives,” “due date passes,” etc. The core of a contract describes the rules by which different sequences of events trigger particular sequences of state transitions in the relationship between the counterparties. By conceptualizing and representing the legal structure of a contract in this way, we expose it to a range of powerful tools and results from the theory of computation. These allow, for example, automated reasoning to determine whether a contract is internally coherent and whether it is complete relative to a particular event alphabet. We illustrate the process by representing a simple loan agreement as an automaton.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2021-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-021-09300-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46590930","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: 7

Copyright Protection in a Digital Environment: Some Introspection 数字环境下的版权保护:一些反思

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-09-15 DOI: 10.2139/ssrn.3924212

J. Kevins

引用次数: 0

Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code 基于深度预训练语言表示模型的无监督法律文章挖掘及其在意大利民法典中的应用

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-09-15 DOI: 10.1007/s10506-021-09301-8

Andrea Tagarelli, Andrea Simeri

{"title":"Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code","authors":"Andrea Tagarelli, Andrea Simeri","doi":"10.1007/s10506-021-09301-8","DOIUrl":"10.1007/s10506-021-09301-8","url":null,"abstract":"<div><p>Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-021-09301-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42634721","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: 26

Correction to: A review of predictive policing from the perspective of fairness 更正:从公平的角度回顾预测性警务

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-08-28 DOI: 10.1007/s10506-021-09299-z

Kiana Alikhademi, Emma Drobina, Diandra Prioleau, Brianna Richardson, Duncan Purves, Juan E. Gilbert

引用次数: 0

Logical English meets legal English for swaps and derivatives 交换和衍生品的逻辑英语与法律英语

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-08-12 DOI: 10.1007/s10506-021-09295-3

Robert Kowalski, Akber Datoo

{"title":"Logical English meets legal English for swaps and derivatives","authors":"Robert Kowalski, Akber Datoo","doi":"10.1007/s10506-021-09295-3","DOIUrl":"10.1007/s10506-021-09295-3","url":null,"abstract":"<div><p>In this paper, we present an informal introduction to Logical English (LE) and illustrate its use to standardise the legal wording of the Automatic Early Termination (AET) clauses of International Swaps and Derivatives Association (ISDA) Agreements. LE can be viewed both as an alternative to conventional legal English for expressing legal documents, and as an alternative to conventional computer languages for automating legal documents. LE is a controlled natural language (CNL), which is designed both to be computer-executable and to be readable by English speakers without special training. The basic form of LE is syntactic sugar for logic programs, in which all sentences have the same standard form, either as rules of the form <i>conclusion if conditions</i> or as unconditional sentences of the form <i>conclusion.</i> However, LE extends normal logic programming by introducing features that are present in other computer languages and other logics. These features include typed variables signalled by common nouns, and existentially quantified variables in the <i>conclusions</i> of sentences signalled by indefinite articles. Although LE translates naturally into a logic programming language such as Prolog or ASP, it can also serve as a neutral standard, which can be compiled into other lower-level computer languages.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2021-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10506-021-09295-3","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47103960","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: 12

PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments PRILJ:一种基于嵌入和聚类的有效的两步法，用于识别法律案件判决中的规则性

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-08-04 DOI: 10.1007/s10506-021-09297-1

Graziella De Martino, Gianvito Pio, Michelangelo Ceci

{"title":"PRILJ: an efficient two-step method based on embedding and clustering for the identification of regularities in legal case judgments","authors":"Graziella De Martino, Gianvito Pio, Michelangelo Ceci","doi":"10.1007/s10506-021-09297-1","DOIUrl":"10.1007/s10506-021-09297-1","url":null,"abstract":"<div><p>In an era characterized by fast technological progress that introduces new unpredictable scenarios every day, working in the law field may appear very difficult, if not supported by the right tools. In this respect, some systems based on Artificial Intelligence methods have been proposed in the literature, to support several tasks in the legal sector. Following this line of research, in this paper we propose a novel method, called PRILJ, that identifies paragraph regularities in legal case judgments, to support legal experts during the redaction of legal documents. Methodologically, PRILJ adopts a two-step approach that first groups documents into clusters, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Embedding-based methods are adopted to properly represent documents and paragraphs into a semantic numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to the paragraphs of a document under preparation. Our extensive experimental evaluation, performed on a real-world dataset provided by EUR-Lex, proves the effectiveness and the efficiency of the proposed method. In particular, its ability of modeling different topics of legal documents, as well as of capturing the semantics of the textual content, appear very beneficial for the considered task, and make PRILJ very robust to the possible presence of noise in the data.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2021-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-021-09297-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43617883","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: 10

A sequence labeling model for catchphrase identification from legal case documents 一种用于法律案件文件口头禅识别的序列标记模型

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-07-30 DOI: 10.1007/s10506-021-09296-2

Arpan Mandal, Kripabandhu Ghosh, Saptarshi Ghosh, Sekhar Mandal

{"title":"A sequence labeling model for catchphrase identification from legal case documents","authors":"Arpan Mandal, Kripabandhu Ghosh, Saptarshi Ghosh, Sekhar Mandal","doi":"10.1007/s10506-021-09296-2","DOIUrl":"10.1007/s10506-021-09296-2","url":null,"abstract":"<div><p>In a Common Law system, legal practitioners need frequent access to prior case documents that discuss relevant legal issues. Case documents are generally very lengthy, containing complex sentence structures, and reading them fully is a strenuous task even for legal practitioners. Having a concise overview of these documents can relieve legal practitioners from the task of reading the complete case statements. Legal catchphrases are (multi-word) phrases that provide a concise overview of the contents of a case document, and automated generation of catchphrases is a challenging problem in legal analytics. In this paper, we propose a novel supervised neural sequence tagging model for the extraction of catchphrases from legal case documents. Specifically, we show that incorporating document-specific information along with a sequence tagging model can enhance the performance of catchphrase extraction. We perform experiments over a set of Indian Supreme Court case documents, for which the gold-standard catchphrases (annotated by legal practitioners) are obtained from a popular legal information system. The performance of our proposed method is compared with that of several existing supervised and unsupervised methods, and our proposed method is empirically shown to be superior to all baselines.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2021-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10506-021-09296-2","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44564003","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: 5

Preserving the rule of law in the era of artificial intelligence (AI) 在人工智能(AI)时代维护法治

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-07-17 DOI: 10.1007/s10506-021-09294-4

Stanley Greenstein

{"title":"Preserving the rule of law in the era of artificial intelligence (AI)","authors":"Stanley Greenstein","doi":"10.1007/s10506-021-09294-4","DOIUrl":"10.1007/s10506-021-09294-4","url":null,"abstract":"<div><p>The study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2021-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-021-09294-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47955647","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: 22

Big Data and Emerging Competition Concerns 大数据和新兴竞争担忧

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2021-07-14 DOI: 10.2139/ssrn.3884350

Aaqib Javeed

引用次数: 0