Artificial Intelligence and Law最新文献

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

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

Self-training improves few-shot learning in legal artificial intelligence tasks 自我训练提高了法律人工智能任务中的少量学习能力

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-05-17 DOI: 10.1007/s10506-024-09403-z

Yulin Zhou, Yongbin Qin, Ruizhang Huang, Yanping Chen, Chuan Lin, Yuan Zhou

引用次数: 0

AI, Law and beyond. A transdisciplinary ecosystem for the future of AI & Law 人工智能、法律及其他。人工智能与法律未来的跨学科生态系统

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-05-16 DOI: 10.1007/s10506-024-09404-y

Floris J. Bex

引用次数: 0

Japanese tort-case dataset for rationale-supported legal judgment prediction 基于理性支持的法律判决预测的日本侵权案件数据集

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-05-11 DOI: 10.1007/s10506-024-09402-0

Hiroaki Yamada, Takenobu Tokunaga, Ryutaro Ohara, Akira Tokutsu, Keisuke Takeshita, Mihoko Sumida

引用次数: 0

InstructPatentGPT: training patent language models to follow instructions with human feedback InstructPatentGPT：训练专利语言模型在人工反馈下遵循指令

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-05-08 DOI: 10.1007/s10506-024-09401-1

Jieh-Sheng Lee

{"title":"InstructPatentGPT: training patent language models to follow instructions with human feedback","authors":"Jieh-Sheng Lee","doi":"10.1007/s10506-024-09401-1","DOIUrl":"10.1007/s10506-024-09401-1","url":null,"abstract":"<div><p>In this research, patent prosecution is conceptualized as a system of reinforcement learning from human feedback. The objective of the system is to increase the likelihood for a language model to generate patent claims that have a higher chance of being granted. To showcase the controllability of the language model, the system learns from granted patents and pre-grant applications with different rewards. The status of “granted” and “pre-grant” are perceived as labeled human feedback implicitly. In addition, specific to patent drafting, the experiments in this research demonstrate the model’s capability to learn from adjusting claim length and inclusion of limiting terms for narrowing claim scope. As proof of concept, the experiments focus on claim ones only and the training data originates from a patent dataset tailored specifically for artificial intelligence. Although the available human feedback in patent prosecution are limited and the quality of generated patent text requires improvement, the experiments following the 3-stage reinforcement learning from human feedback have demonstrated that generative language models are capable of reflecting the human feedback or intent in patent prosecution. To enhance the usability of language models, the implementation in this research utilizes modern techniques that enable execution on a single consumer-grade GPU. The demonstrated proof of concept, which reduces hardware requirements, will prove valuable in the future as more human feedback in patent prosecution become available for broader use, either within patent offices or in the public domain.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 3","pages":"739 - 782"},"PeriodicalIF":3.1,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-024-09401-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140999297","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

Exploring explainable AI in the tax domain 探索税务领域的可解释人工智能

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-05-07 DOI: 10.1007/s10506-024-09395-w

Łukasz Górski, Błażej Kuźniacki, Marco Almada, Kamil Tyliński, Madalena Calvo, Pablo Matias Asnaghi, Luciano Almada, Hilario Iñiguez, Fernando Rubianes, Octavio Pera, Juan Ignacio Nigrelli

{"title":"Exploring explainable AI in the tax domain","authors":"Łukasz Górski, Błażej Kuźniacki, Marco Almada, Kamil Tyliński, Madalena Calvo, Pablo Matias Asnaghi, Luciano Almada, Hilario Iñiguez, Fernando Rubianes, Octavio Pera, Juan Ignacio Nigrelli","doi":"10.1007/s10506-024-09395-w","DOIUrl":"10.1007/s10506-024-09395-w","url":null,"abstract":"<div><p>This paper analyses whether current explainable AI (XAI) techniques can help to address taxpayer concerns about the use of AI in taxation. As tax authorities around the world increase their use of AI-based techniques, taxpayers are increasingly at a loss about whether and how the ensuing decisions follow the procedures required by law and respect their substantive rights. The use of XAI has been proposed as a response to this issue, but it is still an open question whether current XAI techniques are enough to meet existing legal requirements. The paper approaches this question in the context of a case study: a prototype tax fraud detector trained on an anonymized dataset of real-world cases handled by the Buenos Aires (Argentina) tax authority. The decisions produced by this detector are explained through the use of various classification methods, and the outputs of these explanation models are evaluated on their explanatory power and on their compliance with the legal obligation that tax authorities provide the rationale behind their decision-making. We conclude the paper by suggesting technical and legal approaches for designing explanation mechanisms that meet the needs of legal explanation in the tax domain.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 3","pages":"551 - 579"},"PeriodicalIF":3.1,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-024-09395-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141005043","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

The challenge of open-texture in law 法律中开放文本的挑战

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-04-17 DOI: 10.1007/s10506-024-09390-1

Clement Guitton, Aurelia Tamò-Larrieux, Simon Mayer, Gijs van Dijck

{"title":"The challenge of open-texture in law","authors":"Clement Guitton, Aurelia Tamò-Larrieux, Simon Mayer, Gijs van Dijck","doi":"10.1007/s10506-024-09390-1","DOIUrl":"10.1007/s10506-024-09390-1","url":null,"abstract":"<div><p>An important challenge when creating automatically processable laws concerns open-textured terms. The ability to measure open-texture can assist in determining the feasibility of encoding regulation and where additional legal information is required to properly assess a legal issue or dispute. In this article, we propose a novel conceptualisation of open-texture with the aim of determining the extent of open-textured terms in legal documents. We <i>conceptualise open-texture as a lever</i> whose state is impacted by three types of forces: internal forces (the words within the text themselves), external forces (the resources brought to challenge the definition of words), and lateral forces (the merit of such challenges). We tested part of this conceptualisation with 26 participants by investigating agreement in paired annotators. Five key findings emerged. First, agreement on which words are open-texture within a legal text is possible and statistically significant. Second, agreement is even high at an average inter-rater reliability of 0.7 (Cohen’s kappa). Third, when there is agreement on the words, agreement on the Open-Texture Value is high. Fourth, there is a dependence between the Open-Texture Value and reasons invoked behind open-texture. Fifth, involving only four annotators can yield similar results compared to involving twenty more when it comes to only flagging <i>clauses</i> containing open-texture. We conclude the article by discussing limitations of our experiment and which remaining questions in real life cases are still outstanding.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 2","pages":"405 - 435"},"PeriodicalIF":3.1,"publicationDate":"2024-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-024-09390-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140694059","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

Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers? 加密货币证券案件中的大语言模型：GPT 模型能否为律师提供有意义的帮助？

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-04-08 DOI: 10.1007/s10506-024-09399-6

Arianna Trozze, Toby Davies, Bennett Kleinberg

{"title":"Large language models in cryptocurrency securities cases: can a GPT model meaningfully assist lawyers?","authors":"Arianna Trozze, Toby Davies, Bennett Kleinberg","doi":"10.1007/s10506-024-09399-6","DOIUrl":"10.1007/s10506-024-09399-6","url":null,"abstract":"<div><p>Large Language Models (LLMs) could be a useful tool for lawyers. However, empirical research on their effectiveness in conducting legal tasks is scant. We study securities cases involving cryptocurrencies as one of numerous contexts where AI could support the legal process, studying GPT-3.5’s legal reasoning and ChatGPT’s legal drafting capabilities. We examine whether a) GPT-3.5 can accurately determine which laws are potentially being violated from a fact pattern, and b) whether there is a difference in juror decision-making based on complaints written by a lawyer compared to ChatGPT. We feed fact patterns from real-life cases to GPT-3.5 and evaluate its ability to determine correct potential violations from the scenario and exclude spurious violations. Second, we had mock jurors assess complaints written by ChatGPT and lawyers. GPT-3.5’s legal reasoning skills proved weak, though we expect improvement in future models, particularly given the violations it suggested tended to be correct (it merely missed additional, correct violations). ChatGPT performed better at legal drafting, and jurors’ decisions were not statistically significantly associated with the author of the document upon which they based their decisions. Because GPT-3.5 cannot satisfactorily conduct legal reasoning tasks, it would be unlikely to be able to help lawyers in a meaningful way at this stage. However, ChatGPT’s drafting skills (though, perhaps, still inferior to lawyers) could assist lawyers in providing legal services. Our research is the first to systematically study an LLM’s legal drafting and reasoning capabilities in litigation, as well as in securities law and cryptocurrency-related misconduct.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 3","pages":"691 - 737"},"PeriodicalIF":3.1,"publicationDate":"2024-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-024-09399-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140731464","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

Unfair clause detection in terms of service across multiple languages 多语言服务条款中的不公平条款检测

IF 3.1 2区社会学

Artificial Intelligence and Law Pub Date : 2024-04-03 DOI: 10.1007/s10506-024-09398-7

Andrea Galassi, Francesca Lagioia, Agnieszka Jabłonowska, Marco Lippi

{"title":"Unfair clause detection in terms of service across multiple languages","authors":"Andrea Galassi, Francesca Lagioia, Agnieszka Jabłonowska, Marco Lippi","doi":"10.1007/s10506-024-09398-7","DOIUrl":"10.1007/s10506-024-09398-7","url":null,"abstract":"<div><p>Most of the existing natural language processing systems for legal texts are developed for the English language. Nevertheless, there are several application domains where multiple versions of the same documents are provided in different languages, especially inside the European Union. One notable example is given by Terms of Service (ToS). In this paper, we compare different approaches to the task of detecting potential unfair clauses in ToS across multiple languages. In particular, after developing an annotated corpus and a machine learning classifier for English, we consider and compare several strategies to extend the system to other languages: building a novel corpus and training a novel machine learning system for each language, from scratch; projecting annotations across documents in different languages, to avoid the creation of novel corpora; translating training documents while keeping the original annotations; translating queries at prediction time and relying on the English system only. An extended experimental evaluation conducted on a large, original dataset indicates that the time-consuming task of re-building a novel annotated corpus for each language can often be avoided with no significant degradation in terms of performance.\u0000</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"33 3","pages":"641 - 689"},"PeriodicalIF":3.1,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-024-09398-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748324","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