Artificial Intelligence and Law最新文献_第4页

Jurisprudence in hard and soft law output of international organizations: a network analysis of the use of precedent in UN Security Council and general assembly resolutions 国际组织的软硬法律产出的法理学：联合国安理会和大会决议中使用先例的网络分析

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-08-07 DOI: 10.1007/s10506-024-09416-8

Rafael Mesquita, Antonio Pires

{"title":"Jurisprudence in hard and soft law output of international organizations: a network analysis of the use of precedent in UN Security Council and general assembly resolutions","authors":"Rafael Mesquita, Antonio Pires","doi":"10.1007/s10506-024-09416-8","DOIUrl":"10.1007/s10506-024-09416-8","url":null,"abstract":"<div><p>Do hard law international organizations use jurisprudence differently than soft law ones? Precedent can be asset or an encumbrance to international organizations and their members, depending on their aims and on the policy area. Linking current decisions to previously-agreed ones helps to increase cohesion, facilitate consensus among members, and borrow authority – benefits that might be more necessary for some organizations than for others. To compare whether the features of norm-producing organizations correlate with their preference for jurisprudence, we compare two organs from the United Nations system: the Security Council, which produces binding decisions, and the General Assembly, which delivers soft law resolutions. We explore the citation networks formed by the approximately 20,400 resolutions adopted by each organ between 1946 and 2019 to test their differences with regards to the dynamics of citation formation, concentration of citations, and timing. Descriptive results reveal the main periods of jurisprudential activity by the Security Council and the General Assembly, but find no sizeable difference in their overall rate of precedent usage. We apply the Citation Exponential Random Graph Model (cERGM) to test for network determinants of citations and find additional similarities on transitivity and homophily, but also variations regarding preferential attachment.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 4","pages":"1079 - 1108"},"PeriodicalIF":3.1,"publicationDate":"2024-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145493400","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

Classifying proportionality - identification of a legal argument 分类比例-法律论点的识别

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-08-03 DOI: 10.1007/s10506-024-09415-9

Kilian Lüders, Bent Stohlmann

引用次数: 0

Correction: A support system for the detection of abusive clauses in B2C contracts 更正：B2C合同中滥用条款检测支持系统

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-07-24 DOI: 10.1007/s10506-024-09413-x

Sławomir Dadas, Marek Kozłowski, Rafał Poświata, Michał Perełkiewicz, Marcin Białas, Małgorzata Grębowiec

引用次数: 0

Applicability of large language models and generative models for legal case judgement summarization 大语言模型和生成模型在法律案件判决摘要中的适用性

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-07-15 DOI: 10.1007/s10506-024-09411-z

Aniket Deroy, Kripabandhu Ghosh, Saptarshi Ghosh

{"title":"Applicability of large language models and generative models for legal case judgement summarization","authors":"Aniket Deroy, Kripabandhu Ghosh, Saptarshi Ghosh","doi":"10.1007/s10506-024-09411-z","DOIUrl":"10.1007/s10506-024-09411-z","url":null,"abstract":"<div><p>Automatic summarization of legal case judgements, which are known to be long and complex, has traditionally been tried via extractive summarization models. In recent years, generative models including abstractive summarization models and Large language models (LLMs) have gained huge popularity. In this paper, we explore the applicability of such models for legal case judgement summarization. We applied various domain-specific abstractive summarization models and general-domain LLMs as well as extractive summarization models over two sets of legal case judgements – from the United Kingdom (UK) Supreme Court and the Indian Supreme Court – and evaluated the quality of the generated summaries. We also perform experiments on a third dataset of legal documents of a different type – Government reports from the United States. Results show that abstractive summarization models and LLMs generally perform better than the extractive methods as per traditional metrics for evaluating summary quality. However, detailed investigation shows the presence of inconsistencies and hallucinations in the outputs of the generative models, and we explore ways to reduce the hallucinations and inconsistencies in the summaries. Overall, the investigation suggests that further improvements are needed to enhance the reliability of abstractive models and LLMs for legal case judgement summarization. At present, a human-in-the-loop technique is more suitable for performing manual checks to identify inconsistencies in the generated summaries.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 4","pages":"1007 - 1050"},"PeriodicalIF":3.1,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141836870","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

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-07-13 DOI: 10.1007/s10506-024-09410-0

Qiang Ge, Jing Zhang, Xiaoding Guo

{"title":"SIM-GCN: similarity graph convolutional networks for charges prediction","authors":"Qiang Ge, Jing Zhang, Xiaoding Guo","doi":"10.1007/s10506-024-09410-0","DOIUrl":"10.1007/s10506-024-09410-0","url":null,"abstract":"<div><p>In recent years, the analysis of legal judgments and the prediction of outcomes based on case factual descriptions have become hot research topics in the field of judiciary. Among them, the task of charge prediction aims to predict the applicable charges of a judicial case based on its factual description, making it an important research area in the intelligent judiciary. While significant progress has been made in machine learning and deep learning, traditional methods are limited to handling data in Euclidean space and cannot effectively capture the semantic information in the text. To overcome the limitations of traditional learning approaches, many studies have started exploring the use of graphs to represent rich relationships between entities in text and employing graph convolutional neural networks to learn text representations. In this paper, we propose a charge prediction method based on graph convolutional neural networks. By constructing a similarity graph between cases and utilizing graph convolutional neural networks to learn case feature representations, we can better capture the relational information between cases and improve the accuracy of charge prediction. Experimental results on multiple benchmark datasets demonstrate that our proposed model outperforms traditional methods in charge prediction tasks.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 4","pages":"983 - 1005"},"PeriodicalIF":3.1,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141650807","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

From PARIS to LE-PARIS: toward patent response automation with recommender systems and collaborative large language models 从PARIS到LE-PARIS：采用推荐系统和协作式大型语言模型实现专利响应自动化

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-07-12 DOI: 10.1007/s10506-024-09409-7

Jung-Mei Chu, Hao-Cheng Lo, Jieh Hsiang, Chun-Chieh Cho

{"title":"From PARIS to LE-PARIS: toward patent response automation with recommender systems and collaborative large language models","authors":"Jung-Mei Chu, Hao-Cheng Lo, Jieh Hsiang, Chun-Chieh Cho","doi":"10.1007/s10506-024-09409-7","DOIUrl":"10.1007/s10506-024-09409-7","url":null,"abstract":"<div><p>In patent prosecution, timely and effective responses to Office Actions (OAs) are crucial for securing patents. However, past automation and artificial intelligence research have largely overlooked this aspect. To bridge this gap, our study introduces the Patent Office Action Response Intelligence System (PARIS) and its advanced version, the Large Language Model (LLM) Enhanced PARIS (LE-PARIS). These systems are designed to enhance the efficiency of patent attorneys in handling OA responses through collaboration with AI. The systems’ key features include the construction of an OA Topics Database, development of Response Templates, and implementation of Recommender Systems and LLM-based Response Generation. To validate the effectiveness of the systems, we have employed a multi-paradigm analysis using the USPTO Office Action database and longitudinal data based on attorney interactions with our systems over six years. Through five studies, we have examined the constructiveness of OA topics (studies 1 and 2) using topic modeling and our proposed Delphi process, the efficacy of our proposed hybrid LLM-based recommender system tailored for OA responses (study 3), the quality of generated responses (study 4), and the systems’ practical value in real-world scenarios through user studies (study 5). The results indicate that both PARIS and LE-PARIS significantly achieve key metrics and have a positive impact on attorney performance.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 4","pages":"955 - 981"},"PeriodicalIF":3.1,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145493396","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

The digital transformation of jurisprudence: an evaluation of ChatGPT-4’s applicability to solve cases in business law 法学的数字化转型：评估 ChatGPT-4 在解决商业法案件中的适用性

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-07-01 DOI: 10.1007/s10506-024-09406-w

Sascha Schweitzer, Markus Conrads

{"title":"The digital transformation of jurisprudence: an evaluation of ChatGPT-4’s applicability to solve cases in business law","authors":"Sascha Schweitzer, Markus Conrads","doi":"10.1007/s10506-024-09406-w","DOIUrl":"10.1007/s10506-024-09406-w","url":null,"abstract":"<div><p>In the evolving landscape of legal information systems, ChatGPT-4 and other advanced conversational agents (CAs) offer the potential to disruptively transform the law industry. This study evaluates commercially available CAs within the German legal context, thereby assessing the generalizability of previous U.S.-based findings. Employing a unique corpus of 200 distinct legal tasks, ChatGPT-4 was benchmarked against Google Bard, Google Gemini, and its predecessor, ChatGPT-3.5. Human-expert and automated assessments of 4000 CA-generated responses reveal ChatGPT-4 to be the first CA to surpass the threshold of solving realistic legal tasks and passing a German business law exam. While ChatGPT-4 outperforms ChatGPT-3.5, Google Bard, and Google Gemini in both consistency and quality, the results demonstrate a considerable degree of variability, especially in complex cases with no predefined response options. Based on these findings, legal professionals should manually verify all texts produced by CAs before use. Novices must exercise caution with CA-generated legal advice, given the expertise needed for its assessment.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 3","pages":"847 - 871"},"PeriodicalIF":3.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-024-09406-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141707348","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

A support system for the detection of abusive clauses in B2C contracts B2C合同中滥用条款的检测支持系统

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-06-26 DOI: 10.1007/s10506-024-09408-8

Sławomir Dadas, Marek Kozłowski, Rafał Poświata, Michał Perełkiewicz, Marcin Białas, Małgorzata Grębowiec

{"title":"A support system for the detection of abusive clauses in B2C contracts","authors":"Sławomir Dadas, Marek Kozłowski, Rafał Poświata, Michał Perełkiewicz, Marcin Białas, Małgorzata Grębowiec","doi":"10.1007/s10506-024-09408-8","DOIUrl":"10.1007/s10506-024-09408-8","url":null,"abstract":"<div><p>Many countries employ systemic methods of protecting consumers from unfair business practices. One such practice is the use of abusive clauses in business-to-consumer (B2C) contracts, which unfairly impose additional obligations on the consumer or deprive them of their due rights. This article presents an information system that utilizes artificial intelligence methods to automate contract analysis and to detect abusive clauses. The goal of the system is to support the entire administrative process, from contract acquisition, through text extraction and the recommendation of potentially abusive clauses, to the generation of official administrative documents that can be sent to court or to the owners of firms. This article focuses on the components that use machine learning methods. The first is an intelligent crawler that is responsible for automatically detecting contract templates on websites and retrieving them into the system. The second is a document analysis module that implements a clause recommendation algorithm. The algorithm employs transformer-based language models and information retrieval methods to identify abusive passages in text. Our solution achieved first place in a competition on the automatic analysis of B2C contracts organized by the Polish Office of Competition and Consumer Protection (UOKiK), and has since been implemented as an official tool to support the contract analysis process in Poland.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 4","pages":"913 - 951"},"PeriodicalIF":3.1,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-024-09408-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145493401","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

Graph contrastive learning networks with augmentation for legal judgment prediction 用于法律判决预测的具有增强功能的图形对比学习网络

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-06-05 DOI: 10.1007/s10506-024-09407-9

Yao Dong, Xinran Li, Jin Shi, Yongfeng Dong, Chen Chen

引用次数: 0

Intermediate factors and precedential constraint 中间因素和先例制约

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-05-17 DOI: 10.1007/s10506-024-09405-x

Trevor Bench-Capon

引用次数: 0