In this paper, we describe how data mining and hypothesis testing can advance the analysis of originalism in American constitutional law.
在本文中，我们描述了数据挖掘和假设检验如何推动对美国宪法原旨主义的分析。
{"title":"Originalism, hypothesis testing and big data","authors":"John O. McGinnis, Benno Stein","doi":"10.1145/2746090.2746117","DOIUrl":"https://doi.org/10.1145/2746090.2746117","url":null,"abstract":"In this paper, we describe how data mining and hypothesis testing can advance the analysis of originalism in American constitutional law.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134543994","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}
Legal texts consist of hierarchically ordered and labeled (numbered) structural units (sections, subsections, paragraphs, etc.). Using the ordered layout and the labels the different parts of structural units can be easily localized and clearly referred. Nearly one-third of the structural units in the statutes we have examined are list items that can be considered as elliptical. In such cases the list items---each with unique identifying label (number)---are not complete propositions. We have trained the computer to recognize these lists and the different units and elements in them, and to create complete sentences from these. We will introduce some logical considerations that have to be reckoned with if we intend to use these complete sentences to create logical assignments to the legal regulation's content: we show how this technique influences the logical description of norms.
{"title":"Elliptical lists in legislative texts","authors":"Réka Markovich, Syi, Gábor Hamp","doi":"10.1145/2746090.2746112","DOIUrl":"https://doi.org/10.1145/2746090.2746112","url":null,"abstract":"Legal texts consist of hierarchically ordered and labeled (numbered) structural units (sections, subsections, paragraphs, etc.). Using the ordered layout and the labels the different parts of structural units can be easily localized and clearly referred. Nearly one-third of the structural units in the statutes we have examined are list items that can be considered as elliptical. In such cases the list items---each with unique identifying label (number)---are not complete propositions. We have trained the computer to recognize these lists and the different units and elements in them, and to create complete sentences from these. We will introduce some logical considerations that have to be reckoned with if we intend to use these complete sentences to create logical assignments to the legal regulation's content: we show how this technique influences the logical description of norms.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134546930","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}
S. Timmer, J. Meyer, H. Prakken, S. Renooij, Bart Verheij
Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when independencies between variables are involved. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks, the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the correct treatment of statistical evidence is important.
{"title":"A structure-guided approach to capturing bayesian reasoning about legal evidence in argumentation","authors":"S. Timmer, J. Meyer, H. Prakken, S. Renooij, Bart Verheij","doi":"10.1145/2746090.2746093","DOIUrl":"https://doi.org/10.1145/2746090.2746093","url":null,"abstract":"Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. Reasoning about statistics and probabilities in a forensic science setting can be a precarious exercise, especially so when independencies between variables are involved. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. In this paper we focus on the connection between argumentation models and Bayesian belief networks, the latter being a common model to represent and reason with complex probabilistic information. We introduce the notion of a support graph as an intermediate structure between Bayesian networks and argumentation models. A support graph disentangles the complicating graphical properties of a Bayesian network and enhances its intuitive interpretation. Moreover, we show that this model can provide a suitable template for argumentative analysis. Especially in the context of legal reasoning, the correct treatment of statistical evidence is important.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128651421","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}
Erik Hemberg, Jacob B. Rosen, G. Warner, Sanith Wijesinghe, Una-May O’Reilly
We detect tax law abuse by simulating the co-evolution of tax evasion schemes and their discovery through audits. Tax evasion accounts for billions of dollars of lost income each year. When the IRS pursues a tax evasion scheme and changes the tax law or audit procedures, the tax evasion schemes evolve and change into undetectable forms. The arms race between tax evasion schemes and tax authorities presents a serious compliance challenge. Tax evasion schemes are sequences of transactions where each transaction is individually compliant. However, when all transactions are combined they have no other purpose than to evade tax and are thus non-compliant. Our method consists of an ownership network and a sequence of transactions, which outputs the likelihood of conducting an audit, and requires no prior tax return or audit data. We adjust audit procedures for a new generation of evolved tax evasion schemes by simulating the gradual change of tax evasion schemes and audit points, i.e. methods used for detecting non-compliance. Additionally, we identify, for a given audit scoring procedure, which tax evasion schemes will likely escape auditing. The approach is demonstrated in the context of partnership tax law and the Installment Bogus Optional Basis tax evasion scheme. The experiments show the oscillatory behavior of a co-adapting system and that it can model the co-evolution of tax evasion schemes and their detection.
{"title":"Tax non-compliance detection using co-evolution of tax evasion risk and audit likelihood","authors":"Erik Hemberg, Jacob B. Rosen, G. Warner, Sanith Wijesinghe, Una-May O’Reilly","doi":"10.1145/2746090.2746099","DOIUrl":"https://doi.org/10.1145/2746090.2746099","url":null,"abstract":"We detect tax law abuse by simulating the co-evolution of tax evasion schemes and their discovery through audits. Tax evasion accounts for billions of dollars of lost income each year. When the IRS pursues a tax evasion scheme and changes the tax law or audit procedures, the tax evasion schemes evolve and change into undetectable forms. The arms race between tax evasion schemes and tax authorities presents a serious compliance challenge. Tax evasion schemes are sequences of transactions where each transaction is individually compliant. However, when all transactions are combined they have no other purpose than to evade tax and are thus non-compliant. Our method consists of an ownership network and a sequence of transactions, which outputs the likelihood of conducting an audit, and requires no prior tax return or audit data. We adjust audit procedures for a new generation of evolved tax evasion schemes by simulating the gradual change of tax evasion schemes and audit points, i.e. methods used for detecting non-compliance. Additionally, we identify, for a given audit scoring procedure, which tax evasion schemes will likely escape auditing. The approach is demonstrated in the context of partnership tax law and the Installment Bogus Optional Basis tax evasion scheme. The experiments show the oscillatory behavior of a co-adapting system and that it can model the co-evolution of tax evasion schemes and their detection.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115217770","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}
eRulemaking is an ongoing effort to use online tools to foster broader and better public participation in rulemaking --- the multi-step process that federal agencies use to develop new health, safety, and economic regulations. The increasing participation of non-expert citizens, however, has led to a growth in the amount of arguments whose validity or strength are difficult to evaluate, both by the government agencies and fellow citizens. Such arguments typically neglect to provide the reasons for the conclusions and objective evidence for factual claims upon which the arguments are based. In this paper, we propose a novel argumentation model for capturing the evaluability of user comments in eRulemaking. This model is intended to be used for implementing automated systems to assist users in constructing evaluable arguments under online commenting environment for the benefit of quick feedback at a low cost.
{"title":"Toward machine-assisted participation in eRulemaking: an argumentation model of evaluability","authors":"Joonsuk Park, Cheryl Blake, Claire Cardie","doi":"10.1145/2746090.2746118","DOIUrl":"https://doi.org/10.1145/2746090.2746118","url":null,"abstract":"eRulemaking is an ongoing effort to use online tools to foster broader and better public participation in rulemaking --- the multi-step process that federal agencies use to develop new health, safety, and economic regulations. The increasing participation of non-expert citizens, however, has led to a growth in the amount of arguments whose validity or strength are difficult to evaluate, both by the government agencies and fellow citizens. Such arguments typically neglect to provide the reasons for the conclusions and objective evidence for factual claims upon which the arguments are based. In this paper, we propose a novel argumentation model for capturing the evaluability of user comments in eRulemaking. This model is intended to be used for implementing automated systems to assist users in constructing evaluable arguments under online commenting environment for the benefit of quick feedback at a low cost.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114453334","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}
Using Internet Service Provider 'Big' metadata as a case study, we examine legal and ethical issues with machine learning Big Data tools developed and deployed in Australia for law enforcement intelligence purposes. In order to do this, we outline the benefits, limitations and risks of these tools, analyze current methods for de-identification and anonymisation, and discuss necessary safeguards.
{"title":"Using 'big' metadata for criminal intelligence: understanding limitations and appropriate safeguards","authors":"A. Maurushat, L. B. Moses, D. Vaile","doi":"10.1145/2746090.2746110","DOIUrl":"https://doi.org/10.1145/2746090.2746110","url":null,"abstract":"Using Internet Service Provider 'Big' metadata as a case study, we examine legal and ethical issues with machine learning Big Data tools developed and deployed in Australia for law enforcement intelligence purposes. In order to do this, we outline the benefits, limitations and risks of these tools, analyze current methods for de-identification and anonymisation, and discuss necessary safeguards.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124257744","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}
Latifa Al-Abdulkarim, Katie Atkinson, Trevor J. M. Bench-Capon
Abstract Dialetical Frameworks (ADFs) are a recent development in computational argumentation which are, it has been suggested, a fruitful way of implementing theories of case law expressed in terms of factors. In this paper we evaluate this proposal, by representing the CATO analysis using ADFs. We evaluate the ease of implementation, the efficacy of the resulting program, ease of refinement of the program, transparency of the reasoning, relation to formal argumentation techniques, and transferability across domains.
{"title":"Evaluating the use of abstract dialectical frameworks to represent case law","authors":"Latifa Al-Abdulkarim, Katie Atkinson, Trevor J. M. Bench-Capon","doi":"10.1145/2746090.2746111","DOIUrl":"https://doi.org/10.1145/2746090.2746111","url":null,"abstract":"Abstract Dialetical Frameworks (ADFs) are a recent development in computational argumentation which are, it has been suggested, a fruitful way of implementing theories of case law expressed in terms of factors. In this paper we evaluate this proposal, by representing the CATO analysis using ADFs. We evaluate the ease of implementation, the efficacy of the resulting program, ease of refinement of the program, transparency of the reasoning, relation to formal argumentation techniques, and transferability across domains.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122685862","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}
This paper offers a new logical machinery for reasoning about interpretive canons. We identify some options for modelling reasoning about interpretations and show that interpretative argumentation has a distinctive structure where the claim that a legal text ought or may be interpreted in a certain way can be supported or attacked by arguments, whose conflicts may have to be assessed according to further arguments.
{"title":"Deontic defeasible reasoning in legal interpretation: two options for modelling interpretive arguments","authors":"A. Rotolo, Guido Governatori, G. Sartor","doi":"10.1145/2746090.2746100","DOIUrl":"https://doi.org/10.1145/2746090.2746100","url":null,"abstract":"This paper offers a new logical machinery for reasoning about interpretive canons. We identify some options for modelling reasoning about interpretations and show that interpretative argumentation has a distinctive structure where the claim that a legal text ought or may be interpreted in a certain way can be supported or attacked by arguments, whose conflicts may have to be assessed according to further arguments.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132672924","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}
We propose an architecture for a rule-based online management systems (RuleOMS). Typically, many domain areas face the problem that stakeholders maintain databases of their business core information and they have to take decisions or create reports according to guidelines, policies or regulations. To address this issue we propose the integration of databases, in particular relational databases, with a logic reasoner and rule engine. We argue that defeasible logic is an appropriate formalism to model rules, in particular when the rules are meant to model regulations. The resulting RuleOMS provides an efficient and flexible solution to the problem at hand using defeasible inference. A case study of an online child care management system is used to illustrate the proposed architecture.
{"title":"RuleOMS: a rule-based online management system","authors":"M. Islam, Guido Governatori","doi":"10.1145/2746090.2746120","DOIUrl":"https://doi.org/10.1145/2746090.2746120","url":null,"abstract":"We propose an architecture for a rule-based online management systems (RuleOMS). Typically, many domain areas face the problem that stakeholders maintain databases of their business core information and they have to take decisions or create reports according to guidelines, policies or regulations. To address this issue we propose the integration of databases, in particular relational databases, with a logic reasoner and rule engine. We argue that defeasible logic is an appropriate formalism to model rules, in particular when the rules are meant to model regulations. The resulting RuleOMS provides an efficient and flexible solution to the problem at hand using defeasible inference. A case study of an online child care management system is used to illustrate the proposed architecture.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133474362","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}
Matthias Grabmair, Kevin D. Ashley, Ran Chen, Preethi Sureshkumar, Chen Wang, Eric Nyberg, Vern R. Walker
This paper presents first results from a proof of feasibility experiment in conceptual legal document retrieval in a particular domain (involving vaccine injury compensation). The conceptual markup of documents is done automatically using LUIMA, a law-specific semantic extraction toolbox based on the UIMA framework. The system consists of modules for automatic sub-sentence level annotation, machine learning based sentence annotation, basic retrieval using Apache Lucene and a machine learning based reranking of retrieved documents. In a leave-one-out experiment on a limited corpus, the resulting rankings scored higher for most tested queries than baseline rankings created using a commercial full-text legal information system.
{"title":"Introducing LUIMA: an experiment in legal conceptual retrieval of vaccine injury decisions using a UIMA type system and tools","authors":"Matthias Grabmair, Kevin D. Ashley, Ran Chen, Preethi Sureshkumar, Chen Wang, Eric Nyberg, Vern R. Walker","doi":"10.1145/2746090.2746096","DOIUrl":"https://doi.org/10.1145/2746090.2746096","url":null,"abstract":"This paper presents first results from a proof of feasibility experiment in conceptual legal document retrieval in a particular domain (involving vaccine injury compensation). The conceptual markup of documents is done automatically using LUIMA, a law-specific semantic extraction toolbox based on the UIMA framework. The system consists of modules for automatic sub-sentence level annotation, machine learning based sentence annotation, basic retrieval using Apache Lucene and a machine learning based reranking of retrieved documents. In a leave-one-out experiment on a limited corpus, the resulting rankings scored higher for most tested queries than baseline rankings created using a commercial full-text legal information system.","PeriodicalId":309125,"journal":{"name":"Proceedings of the 15th International Conference on Artificial Intelligence and Law","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2015-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130883591","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}
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