Artificial Intelligence and Law最新文献

{"title":"Correction to: A review of predictive policing from the perspective of fairness","authors":"Kiana Alikhademi, Emma Drobina, Diandra Prioleau, Brianna Richardson, Duncan Purves, Juan E. Gilbert","doi":"10.1007/s10506-021-09299-z","DOIUrl":"10.1007/s10506-021-09299-z","url":null,"abstract":"","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"30 1","pages":"19 - 20"},"PeriodicalIF":4.1,"publicationDate":"2021-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10506-021-09299-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48462414","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}

{"title":"Logical English meets legal English for swaps and derivatives","authors":"Robert Kowalski,&nbsp;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":"30 2","pages":"163 - 197"},"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}

{"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,&nbsp;Gianvito Pio,&nbsp;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":"30 3","pages":"359 - 390"},"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}

{"title":"A sequence labeling model for catchphrase identification from legal case documents","authors":"Arpan Mandal,&nbsp;Kripabandhu Ghosh,&nbsp;Saptarshi Ghosh,&nbsp;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":"30 3","pages":"325 - 358"},"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}

{"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":"30 3","pages":"291 - 323"},"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}

{"title":"Big Data and Emerging Competition Concerns","authors":"Aaqib Javeed","doi":"10.2139/ssrn.3884350","DOIUrl":"https://doi.org/10.2139/ssrn.3884350","url":null,"abstract":"This paper identifies access to Big Data as one of the important factors for the success and growth of online platforms. Through Big Data, businesses can track market trends and use target advertising services in ways that were previously impossible. The data can be leveraged to increase market power through a number of artificial intelligence-based advances, thereby increases barriers to entry in the relevant market. Dominant online platforms can use Big Data to enter into certain anti-competitive acts such as price discrimination as well as refuse access to data which can enhance barriers to entry in the relevant market. Hence, this paper seeks to examine the above-mentioned competition concerns and their possible remedies under competition law.","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"110 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2021-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74602186","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}

{"title":"Legal information retrieval for understanding statutory terms","authors":"Jaromír Šavelka,&nbsp;Kevin D. Ashley","doi":"10.1007/s10506-021-09293-5","DOIUrl":"10.1007/s10506-021-09293-5","url":null,"abstract":"<div><p>In this work we study, design, and evaluate computational methods to support interpretation of statutory terms. We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms. The task models the analysis of past treatment of statutory terms, an exercise lawyers routinely perform using a combination of manual and computational approaches. We treat the discovery of sentences as a special case of ad hoc document retrieval. The specifics include retrieval of short texts (sentences), specialized document types (legal case texts), and, above all, the unique definition of document relevance provided in detailed annotation guidelines. To support our experiments we assembled a data set comprising 42 queries (26,959 sentences) which we plan to release to the public in the near future in order to support further research. Most importantly, we investigate the feasibility of developing a system that responds to a query with a list of sentences that mention the term in a way that is useful for understanding and elaborating its meaning. This is accomplished by a systematic assessment of different features that model the sentences’ usefulness for interpretation. We combine features into a compound measure that accounts for multiple aspects. The definition of the task, the assembly of the data set, and the detailed task analysis provide a solid foundation for employing a learning-to-rank approach.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"30 2","pages":"245 - 289"},"PeriodicalIF":4.1,"publicationDate":"2021-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10506-021-09293-5","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50462917","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}

{"title":"Abstract meaning representation for legal documents: an empirical research on a human-annotated dataset","authors":"Sinh Trong Vu,&nbsp;Minh Le Nguyen,&nbsp;Ken Satoh","doi":"10.1007/s10506-021-09292-6","DOIUrl":"10.1007/s10506-021-09292-6","url":null,"abstract":"<div><p>Natural language processing techniques contribute more and more in analyzing legal documents recently, which supports the implementation of laws and rules using computers. Previous approaches in representing a legal sentence often based on logical patterns that illustrate the relations between concepts in the sentence, often consist of multiple words. Those representations cause the lack of semantic information at the word level. In our work, we aim to tackle such shortcomings by representing legal texts in the form of abstract meaning representation (AMR), a graph-based semantic representation that gains lots of polarity in NLP community recently. We present our study in AMR Parsing (producing AMR from natural language) and AMR-to-text Generation (producing natural language from AMR) specifically for legal domain. We also introduce JCivilCode, a human-annotated legal AMR dataset which was created and verified by a group of linguistic and legal experts. We conduct an empirical evaluation of various approaches in parsing and generating AMR on our own dataset and show the current challenges. Based on our observation, we propose our domain adaptation method applying in the training phase and decoding phase of a neural AMR-to-text generation model. Our method improves the quality of text generated from AMR graph compared to the baseline model. (This work is extended from our two previous papers: “An Empirical Evaluation of AMR Parsing for Legal Documents”, published in the Twelfth International Workshop on Juris-informatics (JURISIN) 2018; and “Legal Text Generation from Abstract Meaning Representation”, published in the 32nd International Conference on Legal Knowledge and Information Systems (JURIX) 2019.).</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"30 2","pages":"221 - 243"},"PeriodicalIF":4.1,"publicationDate":"2021-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10506-021-09292-6","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50459383","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}

{"title":"Quantifying the genericness of trademarks using natural language processing: an introduction with suggested metrics","authors":"Cameron Shackell,&nbsp;Lance De Vine","doi":"10.1007/s10506-021-09291-7","DOIUrl":"10.1007/s10506-021-09291-7","url":null,"abstract":"<div><p>If a trademark (“mark”) becomes a generic term, it may be cancelled under trademark law, a process known as genericide. Typically, in genericide cases, consumer surveys are brought into evidence to establish a mark’s semantic status as generic or distinctive. Some drawbacks of surveys are cost, delay, small sample size, lack of reproducibility, and observer bias. Today, however, much discourse involving marks is online. As a potential complement to consumer surveys, therefore, we explore an artificial intelligence approach based chiefly on word embeddings: mathematical models of meaning based on distributional semantics that can be trained on texts selected for jurisdictional and temporal relevance. After identifying two main factors in mark genericness, we first offer a simple screening metric based on the ngram frequency of uncapitalized variants of a mark. We then add two word embedding metrics: one addressing contextual similarity of uncapitalized variants, and one comparing the neighborhood density of marks and known generic terms in a category. For clarity and validation, we illustrate our metrics with examples of genericized, somewhat generic, and distinctive marks such as, respectively, DUMPSTER, DOBRO, and ROLEX.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"30 2","pages":"199 - 220"},"PeriodicalIF":4.1,"publicationDate":"2021-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10506-021-09291-7","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42632629","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}

{"title":"A quantitative approach to ranking corporate law precedents in the Brazilian Superior Court of Justice","authors":"José Luiz Nunes,&nbsp;Ivar A. Hartmann","doi":"10.1007/s10506-021-09290-8","DOIUrl":"10.1007/s10506-021-09290-8","url":null,"abstract":"<div><p>This paper aims to contribute to the goal of finding influential legal precedents by quantitative methods. A lot of work has been made in this direction worldwide, especially in the context of common law jurisdictions. However, this type of work is extremely scarce in the Brazilian literature. In addition, our work also contributes to the research of network analysis and the law by applying these methods to unprecedented amount of data and narrowing our inquiry to a single law area, corporate law. Furthermore, whereas most of the literature applying network analysis to judicial decisions had access to readily available data on the citations to precedent within each ruling, our raw data was nothing but the full text of decisions. We focus on data produced by the Superior Court of Justice (STJ), the highest court in Brazil for matters of federal law, including statutory interpretation of civil, criminal and corporate law. The Court issued an astonishing 282040 opinions tagged as related to corporate law between 2008 and 2018. This amount of cases is unparalleled internationally for superior courts and for studies in network analysis and law. In our results, we rank precedents quantitatively based on the citations they receive and make. We also qualitatively analyze some of the results, especially related to groups identified in the network with the Modularity algorithm. Our findings also reveal that corporate law jurisprudence in the STJ is quantitatively dominated by a few legal issues around one single theme that is only tangentially related to corporate law. That is, a type of contract used for the expansion of telephone landlines, which also allowed the consumer to become a shareholder of the telecommunication company. This comparison is especially pertinent because the utter lack of data on the quantitative weight of STJ precedents means the national literature has been operating in a void of objective measurements, one which has been filled with cherry-picked rulings and subjective ranking criteria.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"30 1","pages":"117 - 145"},"PeriodicalIF":4.1,"publicationDate":"2021-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10506-021-09290-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50514396","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}