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Abstract: Abstract Online dispute resolution (ODR) is an alternative to traditional litigation that can both significantly reduce the disadvantages suffered by litigants unable to afford an attorney and greatly improve court efficiency and economy. An important aspect of many ODR systems is a facilitator, a neutral party who guides the disputants through the steps of reaching an agreement. However, insufficient availability of facilitators impedes broad adoption of ODR systems. This paper describes a novel model of facilitation that integrates two distinct but complementary knowledge sources: cognitive task analysis of facilitator behavior and corpus analysis of ODR session transcripts. This model is implemented in a decision-support system that (1) monitors cases to detect situations requiring immediate attention and (2) automates selection of standard text messages appropriate to the current state of the negotiations. This facilitation model has the potential to compensate for shortages of facilitators by improving the efficiency of experienced facilitators, assisting novice facilitators, and providing autonomous facilitation. PubDate: 2023-09-01 DOI: 10.1007/s10506-022-09318-7
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Abstract: Abstract We implemented a user-centered approach to the design of an artificial intelligence (AI) system that provides users with access to information about the workings of the United States federal court system regardless of their technical background. Presently, most of the records associated with the federal judiciary are provided through a federal system that does not support exploration aimed at discovering systematic patterns about court activities. In addition, many users lack the data analytical skills necessary to conduct their own analyses and convert data into information. We conducted interviews, observations, and surveys to uncover the needs of our users and discuss the development of an intuitive platform informed from these needs that makes it possible for legal scholars, lawyers, and journalists to discover answers to more advanced questions about the federal court system. We report on results from usability testing and discuss design implications for AI and law practitioners and researchers. PubDate: 2023-09-01 DOI: 10.1007/s10506-022-09320-z
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Abstract: Abstract 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 CrossJustice. 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. PubDate: 2023-09-01 DOI: 10.1007/s10506-022-09332-9
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Abstract: Abstract The field of computational law has increasingly moved into the focus of the scientific community, with recent research analysing its issues and risks. In this article, we seek to draw a structured and comprehensive list of societal issues that the deployment of automatically processable regulation could entail. We do this by systematically exploring attributes of the law that are being challenged through its encoding and by taking stock of what issues current projects in this field raise. This article adds to the current literature not only by providing a needed framework to structure arising issues of computational law but also by bridging the gap between theoretical literature and practical implementation. Key findings of this article are: (1) The primary benefit (efficiency vs. accessibility) sought after when encoding law matters with respect to the issues such an endeavor triggers; (2) Specific characteristics of a project—project type, degree of mediation by computers, and potential for divergence of interests—each impact the overall number of societal issues arising from the implementation of automatically processable regulation. PubDate: 2023-09-01 DOI: 10.1007/s10506-022-09323-w
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Abstract: Abstract 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 (legal case retrieval and legal case entailment) 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 relevancy 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 supporting model 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. PubDate: 2023-09-01 DOI: 10.1007/s10506-022-09319-6
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Abstract: Abstract From 2010 to 2020, Indonesia’s tax-to-gross domestic product (GDP) ratio has been declining. A tax-to-GDP ratio trend of this magnitude indicates that the tax authority lacks the capacity to collect taxes. The tax administration system’s modernization utilizing information technology is thus deemed necessary. Artificial intelligence (AI) technology may serve as a solution to this issue. Using the theoretical frameworks of innovations in tax compliance, the cost of taxation, success factors for information technology governance (SFITG), and AI readiness, this study aims to analyze the costs and benefits, the enablers and inhibitors, and the readiness of the government and related parties to apply AI to modernize the tax administration system in Indonesia. This study used qualitative approaches for the data’s collection and analysis. The data were obtained through a literature study and in-depth interviews. The findings show that AI application in the field of taxation can assist tax authorities in enforcing the law, provide taxpayers with convenience in fulfilling their tax obligations, improve justice for all taxpayers, and reduce tax compliance costs. The openness of Indonesia to technological developments, as evidenced by the AI National Strategy, is a supporting factor in the application of AI in Indonesia, particularly for the modernization of the tax administration system. The absence of specific regulations governing AI adoption, as well as a lack of human resources that can help the tax administration process, data, and infrastructure already support, are the impediments to implementing AI for the modernization of the tax administration system in Indonesia. PubDate: 2023-09-01 DOI: 10.1007/s10506-022-09321-y
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Abstract: Abstract This paper presents an approach for legal compliance checking in the Semantic Web which can be effectively applied for applications in the Linked Open Data environment. It is based on modeling deontic norms in terms of ontology classes and ontology property restrictions. It is also shown how this approach can handle norm defeasibility. Such methodology is implemented by decidable fragments of OWL 2, while legal reasoning is carried out by available decidable reasoners. The approach is generalised by presenting patterns for modeling deontic norms and norms compliance checking. PubDate: 2023-09-01 DOI: 10.1007/s10506-022-09317-8
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Abstract: Abstract This article introduces definitions for direct, means-end, oblique (or indirect) and ulterior intent which can be used to test for intent in an algorithmic actor. These definitions of intent are informed by legal theory from common law jurisdictions. Certain crimes exist where the harm caused is dependent on the reason it was done so. Here the actus reus or performative element of the crime is dependent on the mental state or mens rea of the actor. The ability to prosecute these crimes is dependent on the ability to identify and diagnose intentional states in the accused. A certain class of auto didactic algorithmic actor can be given broad objectives without being told how to meet them. Without a definition of intent, they cannot be told not to engage in certain law breaking behaviour nor can they ever be identified as having done it. This ambiguity is neither positive for the owner of the algorithm or for society. The problem exists over and above more familiar debates concerning the eligibility of algorithms for culpability judgements that mens rea is usually associated with. Aside from inchoate offences, many economic crimes with elements of fraud or deceit fall into this category of crime. Algorithms operate in areas where these crimes could be plausibly undertaken depending on whether the intent existed in the algorithm or not. PubDate: 2023-09-01 DOI: 10.1007/s10506-022-09322-x
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Abstract: Abstract Accurate data annotation is essential to successfully implementing machine learning (ML) for regulatory compliance. Annotations allow organizations to train supervised ML algorithms and to adapt and audit the software they buy. The lack of annotation tools focused on regulatory data is slowing the adoption of established ML methodologies and process models, such as CRISP-DM, in various legal domains, including in regulatory compliance. This article introduces Ant, an open-source annotation software for regulatory compliance. Ant is designed to adapt to complex organizational processes and enable compliance experts to be in control of ML projects. By drawing on Business Process Modeling (BPM), we show that Ant can contribute to lift major technical bottlenecks to effectively implement regulatory compliance through software, such as the access to multiple sources of heterogeneous data and the integration of process complexities in the ML pipeline. We provide empirical data to validate the performance of Ant, illustrate its potential to speed up the adoption of ML in regulatory compliance, and highlight its limitations. PubDate: 2023-08-09
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Abstract: Abstract Legal inference is fundamental for building and verifying hypotheses in police investigations. In this study, we build a Natural Language Inference dataset in Korean for the legal domain, focusing on criminal court verdicts. We developed an adversarial hypothesis collection tool that can challenge the annotators and give us a deep understanding of the data, and a hypothesis network construction tool with visualized graphs to show a use case scenario of the developed model. The data is augmented using a combination of Easy Data Augmentation approaches and round-trip translation, as crowd-sourcing might not be an option for datasets with sensible data. We extensively discuss challenges we have encountered, such as the annotator’s limited domain knowledge, issues in the data augmentation process, problems with handling long contexts and suggest possible solutions to the issues. Our work shows that creating legal inference datasets with limited resources is feasible and proposes further research in this area. PubDate: 2023-07-31
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Abstract: Abstract Technology has substantially transformed the way legal services operate in many different countries. With a large and complex collection of digitized legal documents, the judiciary system worldwide presents a promising scenario for the development of intelligent tools. In this work, we tackle the challenging task of organizing and summarizing the constantly growing collection of legal documents, uncovering hidden topics, or themes that later can support tasks such as legal case retrieval and legal judgment prediction. Our approach to this problem relies on topic discovery techniques combined with a variety of preprocessing techniques and learning-based vector representations of words, such as Doc2Vec and BERT-like models. The proposed method was validated using four different datasets composed of short and long legal documents in Brazilian Portuguese, from legal decisions to chapters in legal books. Analysis conducted by a team of legal specialists revealed the effectiveness of the proposed approach to uncover unique and relevant topics from large collections of legal documents, serving many purposes, such as giving support to legal case retrieval tools and also providing the team of legal specialists with a tool that can accelerate their work of labeling/tagging legal documents. PubDate: 2023-07-19
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Abstract: Abstract Access to legal information is fundamental to access to justice. Yet accessibility refers not only to making legal documents available to the public, but also rendering legal information comprehensible to them. A vexing problem in bringing legal information to the public is how to turn formal legal documents such as legislation and judgments, which are often highly technical, to easily navigable and comprehensible knowledge to those without legal education. In this study, we formulate a three-step approach for bringing legal knowledge to laypersons, tackling the issues of navigability and comprehensibility. First, we translate selected sections of the law into snippets (called CLIC-pages), each being a small piece of article that focuses on explaining certain technical legal concept in layperson’s terms. Second, we construct a Legal Question Bank, which is a collection of legal questions whose answers can be found in the CLIC-pages. Third, we design an interactive CLIC Recommender. Given a user’s verbal description of a legal situation that requires a legal solution, CRec interprets the user’s input and shortlists questions from the question bank that are most likely relevant to the given legal situation and recommends their corresponding CLIC pages where relevant legal knowledge can be found. In this paper we focus on the technical aspects of creating an LQB. We show how large-scale pre-trained language models, such as GPT-3, can be used to generate legal questions. We compare machine-generated questions against human-composed questions and find that MGQs are more scalable, cost-effective, and more diversified, while HCQs are more precise. We also show a prototype of CRec and illustrate through an example how our 3-step approach effectively brings relevant legal knowledge to the public. PubDate: 2023-07-06 DOI: 10.1007/s10506-023-09367-6
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Abstract: Abstract Financial risks are among the most important risks in the construction industry projects, which significantly impact project objectives, including project cost. Besides, financial risks have many interactions with each other and project parameters, which must be taken into account to analyze risks correctly. In addition, a source of financial risks in a project is the contract, which is the most important project document. Identifying terms related to financial risks in a contract and considering their effects on the risk management process is an essential issue that has been neglected. Hence, an integrated model for evaluating financial risks and their related contractual clauses were presented. To this end, the effect of financial risks on the project cost was simulated using a system dynamics model. Moreover, terms related to financial risks in a contract text were identified and extracted using text mining, and their effect was included in the system dynamics model. The model was implemented in a hospital construction project in Tehran as a case study, and its results were analyzed. The innovation of the research is integrating text mining and the system dynamics model to investigate the effect of financial risks and related contractual clauses on the project cost. PubDate: 2023-07-04 DOI: 10.1007/s10506-023-09366-7
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Abstract: Abstract The general mitigating and aggravating circumstances of criminal liability are elements attached to the crime that, when they occur, affect the punishment quantum. Cuban criminal legislation provides a catalog of such circumstances and some general conditions for their application. Such norms give judges broad discretion in assessing circumstances and adjusting punishment based on the intensity of those circumstances. In the interest of broad judicial discretion, the law does not establish specific ways for measuring circumstances’ intensity. This gives judges more freedom and autonomy, but it also imposes on them more social responsibility and challenges them to manage the uncertainty and subjectivity inherent in this complex activity. This paper proposes a model to aid the linguistic assessment of circumstances’ intensity and to provide linguistic and numerical recommendations to determine an appropriate punishment interval. M-LAMAC determines the collective evaluation of circumstances of the same type, determines the prevalence of a type of circumstance by means of a compensation function, recommends the required modification in the input interval, and finally recommends a numerical interval adjusted to the judges’ initially expressed preferences. The model’s applicability is demonstrated by means of several experiments on a fictitious case of bank document forgery. PubDate: 2023-07-04 DOI: 10.1007/s10506-023-09365-8
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Abstract: Abstract Despite the public availability of legal documents, there is a need for finding specific information contained in them, such as paragraphs, clauses, items and so on. With such support, users could find more specific information than only finding whole legal documents. Some research efforts have been made in this area, but there is still a lot to be done to have legal information available more easily to be found. Thus, due to the large number of published legal documents and the high degree of connectivity, simple access to the document is not enough. It is necessary to recover the related legal framework for a specific need. In other words, the retrieval of the set of legal documents and their parts related to a specific subject is necessary. Therefore, in this work, we present a proposal of a RDF-based graph to represent and search parts of legal documents, as the output of a set of terms that represents the pursued legal information. Such a proposal is well-grounded on an ontological view, which makes possible to describe the general structure of a legal system and the structure of legal documents, providing this way the grounds for the implementation of the proposed RDF graph in terms of the meaning of their parts and relationships. We posed several queries to retrieve parts of legal documents related to sets of words and the results were significant. PubDate: 2023-07-01 DOI: 10.1007/s10506-023-09364-9
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Abstract: Abstract With the ever-growing accessibility of case law online, it has become challenging to manually identify case law relevant to one’s legal issue. In the Netherlands, the planned increase in the online publication of case law is expected to exacerbate this challenge. In this paper, we tried to predict whether court decisions are cited by other courts or not after being published, thus in a way distinguishing between more and less authoritative cases. This type of system may be used to process the large amounts of available data by filtering out large quantities of non-authoritative decisions, thus helping legal practitioners and scholars to find relevant decisions more easily, and drastically reducing the time spent on preparation and analysis. For the Dutch Supreme Court, the match between our prediction and the actual data was relatively strong (with a Matthews Correlation Coefficient of 0.60). Our results were less successful for the Council of State and the district courts (MCC scores of 0.26 and 0.17, relatively). We also attempted to identify the most informative characteristics of a decision. We found that a completely explainable model, consisting only of handcrafted metadata features, performs almost as well as a less well-explainable system based on all text of the decision. PubDate: 2023-06-28 DOI: 10.1007/s10506-023-09368-5
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Abstract: Abstract Legal documents, like contracts or laws, are subject to interpretation. Different people can have different interpretations of the very same document. Large parts of judicial branches all over the world are concerned with settling disagreements that arise, in part, from these different interpretations. In this context, it only seems natural that during the annotation of legal machine learning data sets, disagreement, how to report it, and how to handle it should play an important role. This article presents an analysis of the current state-of-the-art in the annotation of legal machine learning data sets. The results of the analysis show that all of the analysed data sets remove all traces of disagreement, instead of trying to utilise the information that might be contained in conflicting annotations. Additionally, the publications introducing the data sets often do provide little information about the process that derives the “gold standard” from the initial annotations, often making it difficult to judge the reliability of the annotation process. Based on the state-of-the-art, the article provides easily implementable suggestions on how to improve the handling and reporting of disagreement in the annotation of legal machine learning data sets. PubDate: 2023-06-27 DOI: 10.1007/s10506-023-09369-4
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Abstract: Abstract Identifying, classifying, and analyzing arguments in legal discourse has been a prominent area of research since the inception of the argument mining field. However, there has been a major discrepancy between the way natural language processing (NLP) researchers model and annotate arguments in court decisions and the way legal experts understand and analyze legal argumentation. While computational approaches typically simplify arguments into generic premises and claims, arguments in legal research usually exhibit a rich typology that is important for gaining insights into the particular case and applications of law in general. We address this problem and make several substantial contributions to move the field forward. First, we design a new annotation scheme for legal arguments in proceedings of the European Court of Human Rights (ECHR) that is deeply rooted in the theory and practice of legal argumentation research. Second, we compile and annotate a large corpus of 373 court decisions (2.3M tokens and 15k annotated argument spans). Finally, we train an argument mining model that outperforms state-of-the-art models in the legal NLP domain and provide a thorough expert-based evaluation. All datasets and source codes are available under open lincenses at https://github.com/trusthlt/mining-legal-arguments. PubDate: 2023-06-23 DOI: 10.1007/s10506-023-09361-y
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Abstract: Abstract As lawmakers produce norms, the underlying normative system is affected showing the intrinsic dynamism of law. Through undertaken actions of legal change, the normative system is continuously modified. In a usual legislative practice, the time for an enacted legal provision to be in force may differ from that of its inclusion to the legal system, or from that in which it produces legal effects. Even more, some provisions can produce effects retroactively in time. In this article we study a simulation of such process through the formalisation of a temporalised logical framework upon which a novel belief revision model tackles the dynamic nature of law. Represented through intervals, the temporalisation of sentences allows differentiating the temporal parameters of norms. In addition, a proposed revision operator allows assessing change to the legal system by including a new temporalised literal while preserving the time-based consistency. This can be achieved either by pushing out conflictive pieces of pre-existing norms or through the modification of intervals in which such norms can be either in force, or produce effects. Finally, the construction of the temporalised revision operator is axiomatically characterised and its rational behavior proved through a corresponding representation theorem. PubDate: 2023-06-08 DOI: 10.1007/s10506-023-09363-w
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Abstract: Abstract This article proposes an innovative methodology for enhancing the technical validation, legal alignment and interdisciplinarity of attempts to encode legislation. In the context of an experiment that examines how different legally trained participants convert select provisions of the Australian Copyright Act 1968 (Cth) into machine-executable code, we find that a combination of manual and automated methods for coding validation, which focus on formal adherence to programming languages and conventions, can significantly increase the similarity of encoded rules between coders. Participants nonetheless encountered various interpretive difficulties, including syntactic ambiguity, and intra- and intertextuality, which necessitated legal evaluation, as distinct from and in addition to coding validation. Many of these difficulties can be resolved through what we call a process of ‘legal alignment’ that aims to enhance the congruence between encoded provisions and the true meaning of a statute as determined by the courts. However, some difficulties cannot be overcome in advance, such as factual indeterminacy. Given the inherently interdisciplinary nature of encoding legislation, we argue that it is desirable for ‘rules as code’ (‘RaC’) initiatives to have, at a minimum, legal subject matter, statutory interpretation and technical programming expertise. Overall, we contend that technical validation, legal alignment and interdisciplinary teamwork are integral to the success of attempts to encode legislation. While legal alignment processes will vary depending on jurisdictionally-specific principles and practices of statutory interpretation, the technical and interdisciplinary components of our methodology are transferable across regulatory contexts, bodies of law and Commonwealth and other jurisdictions. PubDate: 2023-06-03 DOI: 10.1007/s10506-023-09350-1
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