Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law最新文献

{"title":"Towards a legal definition of machine intelligence: the argument for artificial personhood in the age of deep learning","authors":"Argyro P. Karanasiou, D. Pinotsis","doi":"10.1145/3086512.3086524","DOIUrl":"https://doi.org/10.1145/3086512.3086524","url":null,"abstract":"The paper dissects the intricacies of Automated Decision Making (ADM) and urges for refining the current legal definition of AI when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. ADM relies upon a plethora of algorithmic approaches and has already found a wide range of applications in marketing automation, social networks, computational neuroscience, robotics, and other fields. Our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm; this can take various shapes and thus yield different answers to key issues regarding agency. The paper offers a fresh look at the concept of \"Machine Intelligence\", which exposes certain vulnerabilities in its current legal interpretation. Most importantly, it further helps us to explore whether the argument for \"artificial personhood\" holds any water. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of Human - Machine interaction and can thus serve as a point of reference for outlining distinct rights and obligations of the programmer and the consumer: driverless cars are used as a case study to explore the several layers of human and machine interaction. These different degrees of automation reflect various levels of complexities in the underlying algorithms, and pose very interesting questions in terms of agency and dynamic tasks carried out by software agents. Part 2 further discusses the intricate nature of the underlying algorithms and artificial neural networks (ANN) that implement them and considers how one can interpret and utilize observed patterns in acquired data. Is \"artificial personhood\" a sufficient legal response to highly sophisticated machine learning techniques employed in decision making that successfully emulate or even enhance human cognitive abilities?","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116835185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness results for popular e-discovery algorithms","authors":"Eugene Yang, D. Grossman, O. Frieder, R. Yurchak","doi":"10.1145/3086512.3086540","DOIUrl":"https://doi.org/10.1145/3086512.3086540","url":null,"abstract":"E-Discovery applications rely upon binary text categorization to determine relevance of documents to a particular case. Although many such categorization algorithms exist, at present, vendors often deploy tools that typically include only one text categorization approach. Unlike previous studies that vary many evaluation parameters simultaneously, fail to include common current algorithms, weights, or features, or used small document collections which are no longer meaningful, we systematically evaluate binary text categorization algorithms using modern benchmark e-Discovery queries (topics) on a benchmark e-Discovery data set. We demonstrate the wide variance of performance obtained using the different parameter combinations, motivating this evaluation. Specifically, we compare five text categorization algorithms, three term weighting techniques and two feature types on a large standard dataset and evaluate the results of this test suite (30 variations) using metrics of greatest interest to the e-Discovery community. Our findings systematically demonstrate that an e-Discovery project is better served by a suite of, rather than a single, algorithms since performance varies greatly depending on the topic, and no approach is uniformly superior across the range of conditions and topics. To that end, we developed an open source project called FreeDiscovery that provides e-Discovery projects with simplified access to a suite of algorithms.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133805569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Compliance patterns: harnessing value modeling and legal interpretation to manage regulatory conversations","authors":"Robert Muthuri, G. Boella, J. Hulstijn, Sara Capecchi, Llio Humphreys","doi":"10.1145/3086512.3086526","DOIUrl":"https://doi.org/10.1145/3086512.3086526","url":null,"abstract":"Companies must be able to demonstrate that their way of doing business is compliant with relevant rules and regulations. However, the law often has open texture; it is generic and needs to be interpreted before it can be applied in a specific case. Entrepreneurs generally lack the expertise to engage in the regulatory conversations that make up this interpretation process. In particular for the application domain of technological startups, this leads to legal risks. This research seeks to develop a robust module for legal interpretation. We apply informal logic to bridge the gap between the principles of interpretation in legal theory with the legal rules that determine compliance of business processes. Accordingly, interpretive arguments characterized by argument schemes are applied to business models represented by value modeling (VDML). The specific outcome of the argumentation process (if any) is then summarized into a compliance pattern, in a context-problem-solution format. A case study from copyright law, about an internet television company, shows that the approach is able to express the legal arguments of the case, but is also understandable for the target audience.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123437851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two-step cascaded textual entailment for legal bar exam question answering","authors":"Mi-Young Kim, R. Goebel","doi":"10.1145/3086512.3086550","DOIUrl":"https://doi.org/10.1145/3086512.3086550","url":null,"abstract":"Our legal question answering system combines legal information retrieval and textual entailment, and exploits semantic information using a logic-based representation. We have evaluated our system using the data from the competition on legal information extraction/entailment (COLIEE)-2017. The competition focuses on the legal information processing required to answer yes/no questions from Japanese legal bar exams, and it consists of two phases: ad hoc legal information retrieval (Phase 1), and textual entailment (Phase 2). Phase 1 requires the identification of Japan civil law articles relevant to a legal bar exam query. For this phase, we have used an information retrieval approach using TF-IDF combined with a simple language model. Phase 2 requires a yes/no decision for previously unseen queries, which we approach by comparing the approximate meanings of queries with relevant statutes. Our meaning extraction process uses a selection of features based on a kind of paraphrase, coupled with a condition/conclusion/exception analysis of articles and queries. We also extract and exploit negation patterns from the articles. We construct a logic-based representation as a semantic analysis result, and then classify questions into easy and difficult types by analyzing the logic representation. If a question is in our easy category, we simply obtain the entailment answer from the logic representation; otherwise we use an unsupervised learning method to obtain the entailment answer. Experimental evaluation shows that our result ranked highest in the Phase 2 amongst all COLIEE-2017 competitors.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124265764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Can machine learning help predict the outcome of asylum adjudications?","authors":"Daniel L. Chen, Jess Eagel","doi":"10.1145/3086512.3086538","DOIUrl":"https://doi.org/10.1145/3086512.3086538","url":null,"abstract":"In this study, we analyzed 492,903 asylum hearings from 336 different hearing locations, rendered by 441 unique judges over a 32 year period from 1981-2013. We define the problem of asylum adjudication prediction as a binary classification task, and using the random forest method developed by Breiman [1], we predict 27 years of refugee decisions. Using only data available up to the decision date, our model correctly classifies 82 percent of all refugee cases by 2013. Our empirical analysis suggests that decision makers exhibit a fair degree of autocorrelation in their rulings, and extraneous factors such as, news and the local weather may be impacting the fate of an asylum seeker. Surprisingly, granting asylum is predominantly driven by trend features and judicial characteristics- features that may seem unfair- and roughly one third-driven by case information, news events, and court information.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115411133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting trade secret case outcomes using argument schemes and learned quantitative value effect tradeoffs","authors":"Matthias Grabmair","doi":"10.1145/3086512.3086521","DOIUrl":"https://doi.org/10.1145/3086512.3086521","url":null,"abstract":"This paper presents the Value Judgment Formalism and its experimental implementation in the VJAP system, which is capable of arguing about, and predicting outcomes of, a set of trade secret misappropriation cases. VJAP creates an argument graph for each case using argument schemes and a representation of values underlying trade secret law and effects of facts on these values. It balances effects on values in each case and analogizes it to tradeoffs in precedents. It predicts case outcomes using a confidence measure computed from the graph and generates textual legal arguments justifying its predictions. The confidence propagation uses quantitative weights learned from past cases using an iterative optimization method. Prediction performance on a limited dataset is competitive with common machine learning models. The results and VJAP's behavior are discussed in detail.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"55 27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115549745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimenting word embeddings in assisting legal review","authors":"Ngoc Phuoc An Vo, C. Privault, Fabien Guillot","doi":"10.1145/3086512.3086531","DOIUrl":"https://doi.org/10.1145/3086512.3086531","url":null,"abstract":"As advanced technologies, such as data mining become part of the everyday workflow of document reviews in litigations, keyword-search still appears to serve as a cornerstone approach in responsive or privilege review. Keywords are conceptually easy to understand and help culling documents at the early stages of the review. But developing proper keywords to minimize the risk of under/over-inclusiveness can lead to complex strategies. To cope with the burden of designing search terms, we propose to use word embedding techniques in a dynamic manner. This paper describes a system leveraging semantic models in a smart review environment in order to support knowledge workers in eDiscovery.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"296 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128635290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On making autonomous vehicles respect traffic law: a case study for dutch law","authors":"H. Prakken","doi":"10.1145/3086512.3086542","DOIUrl":"https://doi.org/10.1145/3086512.3086542","url":null,"abstract":"Among the problems that still need to be solved before autonomous vehicles can fully autonomously participate in traffic is the one of making them respect the traffic laws. This paper discusses this problem by way of a case study of Dutch traffic law. First it is discussed to what extent Dutch traffic law exhibits features that are traditionally said to pose challenges for AI & Law models, such as exceptions, open texture and vagueness and the need for commonsense knowledge. Then three approaches to the design of law-respecting AV are evaluated in light of the challenges posed by Dutch traffic law.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121668246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLIEL: context-based information extraction from commercial law documents","authors":"M. Garcia-Constantino, Katie Atkinson, Danushka Bollegala, Karl Chapman, Frans Coenen, Claire Roberts, Katy Robson","doi":"10.1145/3086512.3086520","DOIUrl":"https://doi.org/10.1145/3086512.3086520","url":null,"abstract":"The effectiveness of document Information Extraction (IE) is greatly affected by the structure and layout of the documents being considered. In the case of legal documents relating to commercial law, an additional challenge is the many different and varied formats, structures and layouts used. In this paper, we present work on a flexible and scalable IE environment, the CLIEL (Commercial Law Information Extraction based on Layout) environment, for application to commercial law documentation that allows layout rules to be derived and then utilised to support IE. The proposed CLIEL environment operates using NLP (Natural Language Processing) techniques, JAPE (Java Annotation Patterns Engine) rules and some GATE (General Architecture for Text Engineering) modules. The system is fully described and evaluated using a commercial law document corpus. The results demonstrate that considering the layout is beneficial for extracting data point instances from legal document collections.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122264443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Formalizing arguments, rules and cases","authors":"Bart Verheij","doi":"10.1145/3086512.3086533","DOIUrl":"https://doi.org/10.1145/3086512.3086533","url":null,"abstract":"Legal argument is typically backed by two kinds of sources: cases and rules. In much AI & Law research, the formalization of arguments, rules and cases has been investigated. In this paper, the tight formal connections between the three are developed further, in an attempt to show that cases can provide the logical basis for establishing which rules and arguments hold in a domain. We use the recently proposed formalism of case models, that has been applied previously to evidential reasoning and ethical systems design. In the present paper, we discuss with respect to case-based modeling how the analogy and distinction between cases can be modeled, and how arguments can be grounded in cases. With respect to rule-based modeling, we discuss conditionality, generality and chaining. With respect to argument-based modeling, we discuss rebutting, undercutting and undermining attack. We evaluate the approach by developing a case model of the rule-based arguments and attacks in Dutch tort law. In this way, we illustrate how statutory, rule-based law from the civil law tradition can be formalized in terms of cases.","PeriodicalId":425187,"journal":{"name":"Proceedings of the 16th edition of the International Conference on Articial Intelligence and Law","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123567924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}