This paper shows how a complex legal doctrinal theory (the doctrine of causation in law) may be represented in a semi-formal, two-layered model of statutory interpretation. The content of the theory is clarified by the proposed knowledge representation. It is argued that doctrinal theories in the reading proposed here are a source of intermediate legal concepts and, in consequence, of rules that enable the judge to argue efficiently in complex cases without entering into wider considerations involving case-based reasoning structures.
{"title":"Incorporation of complex doctrinal theories in a model of statutory interpretation: an example of adequate causal link","authors":"M. Araszkiewicz","doi":"10.1145/2746090.2746114","DOIUrl":"https://doi.org/10.1145/2746090.2746114","url":null,"abstract":"This paper shows how a complex legal doctrinal theory (the doctrine of causation in law) may be represented in a semi-formal, two-layered model of statutory interpretation. The content of the theory is clarified by the proposed knowledge representation. It is argued that doctrinal theories in the reading proposed here are a source of intermediate legal concepts and, in consequence, of rules that enable the judge to argue efficiently in complex cases without entering into wider considerations involving case-based reasoning structures.","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":"125584191","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}
The Brazilian Supreme Court is one of the largest in the world in terms of case load. Since 1988 more than 1.5 million cases have reached the court, which is comprised of eleven Justices, mostly through appeal. This study describes the Supremo 2.0 ('Supreme Court' 2.0) project, undertaken to allow fast and interactive visualization of this case load. We describe the technologies and algorithms employed and outline its general functioning. We discuss the benefits of intuitive visualization, cross-filtering and multiple-view systems for knowledge discovery.
{"title":"Visualizing Brazilian justice: the supreme court 2.0 project","authors":"Daniel Chada, Felipe A. Silva, Patrícia Borges","doi":"10.1145/2746090.2746113","DOIUrl":"https://doi.org/10.1145/2746090.2746113","url":null,"abstract":"The Brazilian Supreme Court is one of the largest in the world in terms of case load. Since 1988 more than 1.5 million cases have reached the court, which is comprised of eleven Justices, mostly through appeal. This study describes the Supremo 2.0 ('Supreme Court' 2.0) project, undertaken to allow fast and interactive visualization of this case load. We describe the technologies and algorithms employed and outline its general functioning. We discuss the benefits of intuitive visualization, cross-filtering and multiple-view systems for knowledge discovery.","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":"131842703","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 extended abstract describes a web-based system that helps lawyers and their clients save time, effort and money. The system automatically reviews a contract, written in English, and points out which components are present and which components are not against a given gold standard. The system also gives users feedback to improve the contract and it is, to some extent, interactive.
{"title":"Writing and reviewing contracts: don't you wish to save time, effort, and money?","authors":"Jason Gabbard, J. Sukkarieh, Federico Silva","doi":"10.1145/2746090.2746534","DOIUrl":"https://doi.org/10.1145/2746090.2746534","url":null,"abstract":"This extended abstract describes a web-based system that helps lawyers and their clients save time, effort and money. The system automatically reviews a contract, written in English, and points out which components are present and which components are not against a given gold standard. The system also gives users feedback to improve the contract and it is, to some extent, interactive.","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":"132293477","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}
In a criminal trial, a judge or jury needs to reach a conclusion about 'what happened' based on the available evidence. Often this includes probabilistic evidence. Whereas Bayesian networks form a good tool for analysing evidence probabilistically, simply presenting the outcome of the network to a judge or jury does not allow them to make an informed decision. In this paper, we propose to combine Bayesian networks with a narrative approach to reasoning with legal evidence, the result of which allows a juror to reason with alternative scenarios while also incorporating probabilistic information. The proposed method aids both the construction and the understanding of Bayesian networks, using scenario schemes. We make three distinct contributions: (1) we propose to use scenario schemes to aid the construction of Bayesian networks, (2) we propose a method for producing scenarios in text form from the resulting networks and (3) we propose a format for reporting the alternative scenarios and their relations to the evidence (including strength).
{"title":"Constructing and understanding Bayesian networks for legal evidence with scenario schemes","authors":"C. Vlek, H. Prakken, S. Renooij, Bart Verheij","doi":"10.1145/2746090.2746097","DOIUrl":"https://doi.org/10.1145/2746090.2746097","url":null,"abstract":"In a criminal trial, a judge or jury needs to reach a conclusion about 'what happened' based on the available evidence. Often this includes probabilistic evidence. Whereas Bayesian networks form a good tool for analysing evidence probabilistically, simply presenting the outcome of the network to a judge or jury does not allow them to make an informed decision. In this paper, we propose to combine Bayesian networks with a narrative approach to reasoning with legal evidence, the result of which allows a juror to reason with alternative scenarios while also incorporating probabilistic information. The proposed method aids both the construction and the understanding of Bayesian networks, using scenario schemes. We make three distinct contributions: (1) we propose to use scenario schemes to aid the construction of Bayesian networks, (2) we propose a method for producing scenarios in text form from the resulting networks and (3) we propose a format for reporting the alternative scenarios and their relations to the evidence (including strength).","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":"129003176","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}
In the process of proof alternative stories that explain 'what happened' in a case are tested using arguments based on evidence. Building on the author's earlier hybrid theory, this paper presents a formal theory that combines causal stories and evidential arguments, further integrating the different types of reasoning in a framework for structured argumentation. This then allows for correct reasoning with causal and evidential rules, and further integrates arguments and stories by grounding them both in well-known dialectical argumentation semantics.
{"title":"An integrated theory of causal stories and evidential arguments","authors":"Floris Bex","doi":"10.1145/2746090.2746094","DOIUrl":"https://doi.org/10.1145/2746090.2746094","url":null,"abstract":"In the process of proof alternative stories that explain 'what happened' in a case are tested using arguments based on evidence. Building on the author's earlier hybrid theory, this paper presents a formal theory that combines causal stories and evidential arguments, further integrating the different types of reasoning in a framework for structured argumentation. This then allows for correct reasoning with causal and evidential rules, and further integrates arguments and stories by grounding them both in well-known dialectical argumentation semantics.","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":"124761324","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}
M. Araszkiewicz, Agata Lopatkiewicz, Adam Zienkiewicz, Tomasz Zurek
This paper evaluates the Parenting Plan Support System, a partially implemented decision support system designed to help parents to draft an agreement concerning relations with their children after the divorce, against the background of a real-life case. The focus here is on knowledge representation issues and the functioning of the inference engine.
{"title":"Representation of an actual divorce dispute in the parenting plan support system","authors":"M. Araszkiewicz, Agata Lopatkiewicz, Adam Zienkiewicz, Tomasz Zurek","doi":"10.1145/2746090.2746119","DOIUrl":"https://doi.org/10.1145/2746090.2746119","url":null,"abstract":"This paper evaluates the Parenting Plan Support System, a partially implemented decision support system designed to help parents to draft an agreement concerning relations with their children after the divorce, against the background of a real-life case. The focus here is on knowledge representation issues and the functioning of the inference engine.","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":"116003314","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
Reasoning about statistics and probabilities can, when not treated with cautiousness, lead to reasoning errors. Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. Uncertainties are by forensic experts often expressed numerically, but lawyers, judges and other legal experts have notorious difficulty interpreting these results [3, 1, 2, 5]. In this demonstration of our main paper [6] we focus on the connection between formal models of argumentation and Bayesian belief networks (BNs). We use BNs because they are a well-known 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 captures the inferences modelled in a Bayesian network but disentangles the complicating graphical properties of such models and instead emphasises its intuitive understanding. Moreover, we show that this intermediate model can function as a template to generate different arguments based on the data.
{"title":"Demonstration of 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":"10.1145/2746090.2750370","DOIUrl":"https://doi.org/10.1145/2746090.2750370","url":null,"abstract":"Reasoning about statistics and probabilities can, when not treated with cautiousness, lead to reasoning errors. Over the last decades the rise of forensic sciences has led to an increase in the availability of statistical evidence. To facilitate the correct explanation of such evidence we investigate how argumentation models can help in the interpretation of statistical information. Uncertainties are by forensic experts often expressed numerically, but lawyers, judges and other legal experts have notorious difficulty interpreting these results [3, 1, 2, 5]. In this demonstration of our main paper [6] we focus on the connection between formal models of argumentation and Bayesian belief networks (BNs). We use BNs because they are a well-known 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 captures the inferences modelled in a Bayesian network but disentangles the complicating graphical properties of such models and instead emphasises its intuitive understanding. Moreover, we show that this intermediate model can function as a template to generate different arguments based on the data.","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":"127796975","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}
Much legal evidence is being generated by and stored in information systems. In this paper we look at evidence from an auditing point of view. Auditors rely on evidence of the party being audited, who may have a legitimate or illegitimate interest to manipulate it. To assess the quality of audit evidence, we argue for an approach called model-based auditing. It is based on a mathematically precise model of the expected relationships between the flow of money and the flow of goods or services. Such equations are used for cross verification. If the equations do not hold, either something is wrong (violation) or some underlying assumption is false (exception). To show the usefulness of the approach, we look in particular at a case study of a legal dispute about automated contract monitoring. A precise revenue model is instrumental in demonstrating that the data set does indeed constitute appropriate evidence to settle the case.
{"title":"Reliability of electronic evidence: an application for model-based auditing","authors":"R. Christiaanse, P. Griffioen, J. Hulstijn","doi":"10.1145/2746090.2746098","DOIUrl":"https://doi.org/10.1145/2746090.2746098","url":null,"abstract":"Much legal evidence is being generated by and stored in information systems. In this paper we look at evidence from an auditing point of view. Auditors rely on evidence of the party being audited, who may have a legitimate or illegitimate interest to manipulate it. To assess the quality of audit evidence, we argue for an approach called model-based auditing. It is based on a mathematically precise model of the expected relationships between the flow of money and the flow of goods or services. Such equations are used for cross verification. If the equations do not hold, either something is wrong (violation) or some underlying assumption is false (exception). To show the usefulness of the approach, we look in particular at a case study of a legal dispute about automated contract monitoring. A precise revenue model is instrumental in demonstrating that the data set does indeed constitute appropriate evidence to settle the case.","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":"128706630","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}
The intelligent trademark analysis system developed by TrademarkNow is a trademark information system based on an AI model of trademark similarity (likelihood of confusion). The basic technology can be used as a general trademark search engine as well as for more specific purposes ranging from trademark watching (TrademarkNow NameWatch) to comprehensive risk analysis (TrademarkNow NameCheck).
{"title":"AI analysis of trademark law: trademarknow NameCheck and NameWatch","authors":"A. Ronkainen","doi":"10.1145/2746090.2746535","DOIUrl":"https://doi.org/10.1145/2746090.2746535","url":null,"abstract":"The intelligent trademark analysis system developed by TrademarkNow is a trademark information system based on an AI model of trademark similarity (likelihood of confusion). The basic technology can be used as a general trademark search engine as well as for more specific purposes ranging from trademark watching (TrademarkNow NameWatch) to comprehensive risk analysis (TrademarkNow NameCheck).","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":"123568064","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}
In a pair of papers from 1995 and 1997, I developed a computational theory of legal argument, but left open a question about the key concept of a "prototype." Contemporary trends in machine learning have now shed new light on the subject. In this paper, I will describe my recent work on "manifold learning," as well as some work in progress on "deep learning." Taken together, this work leads to a logical language grounded in a prototypical perceptual semantics, with implications for legal theory. The main technical contribution of the paper is a categorical logic based on the category of differential manifolds (Man), which is weaker than a logic based on the category of sets (Set) or the category of topological spaces (Top). The paper also shows how this logic can be extended to a full Language for Legal Discourse (LLD), and suggests a solution to the elusive problem of "coherence" in legal argument.
{"title":"How to ground a language for legal discourse in a prototypical perceptual semantics","authors":"L. McCarty","doi":"10.1145/2746090.2746091","DOIUrl":"https://doi.org/10.1145/2746090.2746091","url":null,"abstract":"In a pair of papers from 1995 and 1997, I developed a computational theory of legal argument, but left open a question about the key concept of a \"prototype.\" Contemporary trends in machine learning have now shed new light on the subject. In this paper, I will describe my recent work on \"manifold learning,\" as well as some work in progress on \"deep learning.\" Taken together, this work leads to a logical language grounded in a prototypical perceptual semantics, with implications for legal theory. The main technical contribution of the paper is a categorical logic based on the category of differential manifolds (Man), which is weaker than a logic based on the category of sets (Set) or the category of topological spaces (Top). The paper also shows how this logic can be extended to a full Language for Legal Discourse (LLD), and suggests a solution to the elusive problem of \"coherence\" in legal argument.","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":"126959134","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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