Artificial Intelligence and Law最新文献_第8页

Correction: thirty years of Artificial Intelligence and Law: the second decade 更正:人工智能和法律的三十年:第二个十年

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-11-05 DOI: 10.1007/s10506-022-09333-8

Giovanni Sartor, Michał Araszkiewicz, Katie Atkinson, Floris Bex, Tom van Engers, Enrico Francesconi, Henry Prakken, Giovanni Sileno, Frank Schilder, Adam Wyner, Trevor Bench-Capon

引用次数: 0

Judicial knowledge-enhanced magnitude-aware reasoning for numerical legal judgment prediction 司法知识增强的量值感知推理用于数字法律判决预测

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-11-02 DOI: 10.1007/s10506-022-09337-4

Sheng Bi, Zhiyao Zhou, Lu Pan, Guilin Qi

{"title":"Judicial knowledge-enhanced magnitude-aware reasoning for numerical legal judgment prediction","authors":"Sheng Bi, Zhiyao Zhou, Lu Pan, Guilin Qi","doi":"10.1007/s10506-022-09337-4","DOIUrl":"10.1007/s10506-022-09337-4","url":null,"abstract":"<div><p>Legal Judgment Prediction (LJP) is an essential component of legal assistant systems, which aims to automatically predict judgment results from a given criminal fact description. As a vital subtask of LJP, researchers have paid little attention to the numerical LJP, i.e., the prediction of imprisonment and penalty. Existing methods ignore numerical information in the criminal facts, making their performances far from satisfactory. For instance, the amount of theft varies, as do the prison terms and penalties. The major challenge is how the model can obtain the ability of numerical comparison and magnitude perception, e.g., 400 < 500 < 800, 500 is closer to 400 than to 800. To this end, we propose a judicial knowledge-enhanced magnitude-aware reasoning architecture, called NumLJP, for the numerical LJP task. Specifically, we first implement a contrastive learning-based judicial knowledge selector to distinguish confusing criminal cases efficiently. Unlike previous approaches that employ the law article as external knowledge, judicial knowledge is a quantitative guideline in real scenarios. It contains many numerals (called anchors) that can construct a reference frame. Then we design a masked numeral prediction task to help the model remember these anchors to acquire legal numerical commonsense from the selected judicial knowledge. We construct a scale-based numerical graph using the anchors and numerals in facts to perform magnitude-aware numerical reasoning. Finally, the representations of fact description, judicial knowledge, and numerals are fused to make decisions. We conduct extensive experiments on three real-world datasets and select several competitive baselines. The results demonstrate that the macro-F1 of NumLJP improves by at least 9.53% and 11.57% on the prediction of penalty and imprisonment, respectively.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"31 4","pages":"773 - 806"},"PeriodicalIF":4.1,"publicationDate":"2022-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48823384","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: 2

Two factor-based models of precedential constraint: a comparison and proposal 两种基于因素的优先约束模型:比较与建议

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-11-01 DOI: 10.1007/s10506-022-09335-6

Robert Mullins

{"title":"Two factor-based models of precedential constraint: a comparison and proposal","authors":"Robert Mullins","doi":"10.1007/s10506-022-09335-6","DOIUrl":"10.1007/s10506-022-09335-6","url":null,"abstract":"<div><p>The article considers two different interpretations of the reason model of precedent pioneered by John Horty. On a plausible interpretation of the reason model, past cases provide reasons to prioritize reasons favouring the same outcome as a past case over reasons favouring the opposing outcome. Here I consider the merits of this approach to the role of precedent in legal reasoning in comparison with a closely related view favoured by some legal theorists, according to which past cases provide reasons for undercutting (or ‘excluding’) reasons favouring the opposing outcome. After embedding both accounts within a general default logic, I note some important differences between the two approaches that emerge as a result of plausible distinctions between rebutting and undercutting defeat in formal models of legal reasoning. These differences stem from the ‘preference independence’ of undercutting defeat . Undercutting reasons succeed in defeating opposing reasons irrespective of their relative strength. As a result, the two accounts differ in their account of the way in which precedents constrain judicial reasoning. I conclude by suggesting that the two approaches can be integrated within a single model, in which the distinction between undercutting and rebutting defeat is used to account for the distinction between strict and persuasive forms of precedential constraint.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"31 4","pages":"703 - 738"},"PeriodicalIF":4.1,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-022-09335-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44509126","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

Masked prediction and interdependence network of the law using data from large-scale Japanese court judgments 基于大规模日本法院判决数据的法律预测与相互依赖网络

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-10-21 DOI: 10.1007/s10506-022-09336-5

Ryoma Kondo, Takahiro Yoshida, Ryohei Hisano

{"title":"Masked prediction and interdependence network of the law using data from large-scale Japanese court judgments","authors":"Ryoma Kondo, Takahiro Yoshida, Ryohei Hisano","doi":"10.1007/s10506-022-09336-5","DOIUrl":"10.1007/s10506-022-09336-5","url":null,"abstract":"<div><p>Court judgments contain valuable information on how statutory laws and past court precedents are interpreted and how the interdependence structure among them evolves in the courtroom. Data-mining the evolving structure of such customs and norms that reflect myriad social values from a large-scale court judgment corpus is an essential task from both the academic and industrial perspectives. In this paper, using data from approximately 110,000 court judgments from Japan spanning the period 1998–2018 from the district to the supreme court level, we propose two tasks that grasp such a structure from court judgments and highlight the strengths and weaknesses of major machine learning models. One is a prediction task based on masked language modeling that connects textual information to legal codes and past court precedents. Another is a dynamic link prediction task where we predict the hidden interdependence structure in the law. We make quantitative and qualitative comparisons among major machine learning models to obtain insights for future developments.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"31 4","pages":"739 - 771"},"PeriodicalIF":4.1,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-022-09336-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42318044","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

Knowledge mining and social dangerousness assessment in criminal justice: metaheuristic integration of machine learning and graph-based inference 刑事司法中的知识挖掘与社会危险性评估:机器学习与基于图的推理的元启发式集成

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-10-20 DOI: 10.1007/s10506-022-09334-7

Nicola Lettieri, Alfonso Guarino, Delfina Malandrino, Rocco Zaccagnino

{"title":"Knowledge mining and social dangerousness assessment in criminal justice: metaheuristic integration of machine learning and graph-based inference","authors":"Nicola Lettieri, Alfonso Guarino, Delfina Malandrino, Rocco Zaccagnino","doi":"10.1007/s10506-022-09334-7","DOIUrl":"10.1007/s10506-022-09334-7","url":null,"abstract":"<div><p>One of the main challenges for computational legal research is drawing up innovative heuristics to derive actionable knowledge from legal documents. While a large part of the research has been so far devoted to the extraction of purely legal information, less attention has been paid to seeking out in the texts the clues of more complex entities: legally relevant facts whose detection requires to link and interpret, as a unified whole, legal information and results of empirical analyses. This paper presents an ongoing research that points in this direction, trying to devise new ways to support public prosecutors in assessing the dangerousness of individuals and groups under investigation, an activity that precisely relies on the cross-sectional evaluation of legal and empirical data. A knowledge mining strategy will be outlined that lines up, into a single metaheuristic model, information extraction, network-based inference, machine learning and visual analytics. We will focus, in particular, on the integration of graph-based inference and machine learning methods used both to support classification tasks and to explore new forms of man-machine cooperation. Experiments made involving public prosecutors from the Italian Anti-Mafia Investigation Directorate and using data from real investigations have not only shown the potentialities of our approach but also offered an opportunity to reflect on the role we could assign to AI when thinking about the future of legal science and practice.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"31 4","pages":"653 - 702"},"PeriodicalIF":4.1,"publicationDate":"2022-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43244036","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: 2

Analogical lightweight ontology of EU criminal procedural rights in judicial cooperation 欧盟司法合作中刑事诉讼权利的类比轻量本体论

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-10-13 DOI: 10.1007/s10506-022-09332-9

Davide Audrito, Emilio Sulis, Llio Humphreys, Luigi Di Caro

{"title":"Analogical lightweight ontology of EU criminal procedural rights in judicial cooperation","authors":"Davide Audrito, Emilio Sulis, Llio Humphreys, Luigi Di Caro","doi":"10.1007/s10506-022-09332-9","DOIUrl":"10.1007/s10506-022-09332-9","url":null,"abstract":"<div><p>This article describes the creation of a lightweight ontology of European Union (EU) criminal procedural rights in judicial cooperation. The ontology is intended to help legal practitioners understand the precise contextual meaning of terms as well as helping to inform the creation of a rule ontology of criminal procedural rights in judicial cooperation. In particular, we started from the problem that directives sometimes do not contain articles dedicated to definitions. This issue provided us with an opportunity to explore a phenomenon typically neglected in the construction of domain-specific legal ontologies. Whether classical definitions are present or absent, laws and legal sources in general are typically peppered with a number of hidden definitions (in the sense that they are not clearly marked out as such) as well as incomplete definitions, which may nevertheless help legal practitioners (and legal reasoning systems) to reason on the basis of analogy or teleology. In this article we describe the theoretical basis for building an analogical lightweight ontology in the framework of an EU project called <i>CrossJustice</i>. We present our methodology for collecting the data, extracting the data fields and creating the ontology with WebProtégé, followed by our conclusions and ideas for future work.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"31 3","pages":"629 - 652"},"PeriodicalIF":4.1,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10506-022-09332-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44832526","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: 2

Policing based on automatic facial recognition 基于人脸自动识别的警务

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-09-10 DOI: 10.1007/s10506-022-09330-x

Zhilong Guo, Lewis Kennedy

{"title":"Policing based on automatic facial recognition","authors":"Zhilong Guo, Lewis Kennedy","doi":"10.1007/s10506-022-09330-x","DOIUrl":"10.1007/s10506-022-09330-x","url":null,"abstract":"<div><p>Advances in technology have transformed and expanded the ways in which policing is run. One new manifestation is the mass acquisition and processing of private facial images via automatic facial recognition by the police: what we conceptualise as AFR-based policing. However, there is still a lack of clarity on the manner and extent to which this largely-unregulated technology is used by law enforcement agencies and on its impact on fundamental rights. Social understanding and involvement are still insufficient in the context of AFR technologies, which in turn affects social trust in and legitimacy and effectiveness of intelligent governance. This article delineates the function creep of this new concept, identifying the individual and collective harms it engenders. A technological, contextual perspective of the function creep of AFR in policing will evidence the comprehensive creep of training datasets and learning algorithms, which have by-passed an ignorant public. We thus argue individual harms to dignity, privacy and autonomy, combine to constitute a form of cultural harm, impacting directly on individuals and society as a whole. While recognising the limitations of what the law can achieve, we conclude by considering options for redress and the creation of an enhanced regulatory and oversight framework model, or Code of Conduct, as a means of encouraging cultural change from prevailing police indifference to enforcing respect for the human rights violations potentially engaged. The imperative will be to strengthen the top-level design and technical support of AFR policing, imbuing it with the values implicit in the rule of law, democratisation and scientisation-to enhance public confidence and trust in AFR social governance, and to promote civilised social governance in AFR policing.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"31 2","pages":"397 - 443"},"PeriodicalIF":4.1,"publicationDate":"2022-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45170053","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: 3

Thirty years of Artificial Intelligence and Law: the first decade 人工智能与法律三十年:第一个十年

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-09-06 DOI: 10.1007/s10506-022-09329-4

Guido Governatori, Trevor Bench-Capon, Bart Verheij, Michał Araszkiewicz, Enrico Francesconi, Matthias Grabmair

引用次数: 5

SM-BERT-CR: a deep learning approach for case law retrieval with supporting model SM-BERT-CR：一种具有支持模型的判例法检索深度学习方法

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-08-10 DOI: 10.1007/s10506-022-09319-6

Yen Thi-Hai Vuong, Quan Minh Bui, Ha-Thanh Nguyen, Thi-Thu-Trang Nguyen, Vu Tran, Xuan-Hieu Phan, Ken Satoh, Le-Minh Nguyen

{"title":"SM-BERT-CR: a deep learning approach for case law retrieval with supporting model","authors":"Yen Thi-Hai Vuong, Quan Minh Bui, Ha-Thanh Nguyen, Thi-Thu-Trang Nguyen, Vu Tran, Xuan-Hieu Phan, Ken Satoh, Le-Minh Nguyen","doi":"10.1007/s10506-022-09319-6","DOIUrl":"10.1007/s10506-022-09319-6","url":null,"abstract":"<div><p>Case law retrieval is the task of locating truly relevant legal cases given an input query case. Unlike information retrieval for general texts, this task is more complex with two phases (<i>legal case retrieval</i> and <i>legal case entailment</i>) and much harder due to a number of reasons. First, both the query and candidate cases are long documents consisting of several paragraphs. This makes it difficult to model with representation learning that usually has restriction on input length. Second, the concept of <i>relevancy</i> in this domain is defined based on the legal relation that goes beyond the lexical or topical relevance. This is a real challenge because normal text matching will not work. Third, building a large and accurate legal case dataset requires a lot of effort and expertise. This is obviously an obstacle to creating enough data for training deep retrieval models. In this paper, we propose a novel approach called <i>supporting model</i> that can deal with both phases. The underlying idea is the case–case supporting relation and the paragraph–paragraph as well as the decision-paragraph matching strategy. In addition, we propose a method to automatically create a large weak-labeling dataset to overcome the lack of data. The experiments showed that our solution has achieved the state-of-the-art results for both case retrieval and case entailment phases.</p></div>","PeriodicalId":51336,"journal":{"name":"Artificial Intelligence and Law","volume":"31 3","pages":"601 - 628"},"PeriodicalIF":4.1,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49470933","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: 11

Thirty years of artificial intelligence and law: the third decade 人工智能与法律的三十年：第三个十年

IF 4.1 2区社会学

Artificial Intelligence and Law Pub Date : 2022-08-09 DOI: 10.1007/s10506-022-09327-6

Serena Villata, Michal Araszkiewicz, Kevin Ashley, Trevor Bench-Capon, L. Karl Branting, Jack G. Conrad, Adam Wyner

引用次数: 13