This textbook presents an overview of the critically important ethical and legal issues that arise in the computing field and provides a professional perspective from software engineering. The author gained exposure to these aspects of computing while working as a software engineer at Motorola in Ireland, where he coordinated the patent programme and worked with several software suppliers. Topics and features: Presents a broad overview of ethics and the law Includes key learning topics, summaries, and review (...) questions in each chapter, together with a useful glossary Discusses the professional responsibility of computer professionals Explores ethics in various civilisations and religious traditions Discusses ethical software engineering and ethical outsourcing Considers what is fair and ethical in data science Describes ethical challenges that arise in social media and the AI field Reviews intellectual property including patents, copyright and trademarks This practical and easy-to-follow textbook/reference is ideal for computer science students seeking to understand legal and ethical aspects of computing. The text also serves as a concise self-study primer for software engineers and software managers. (shrink)

Brain-Computer Interface is a set of technologies that are of increasing interest to researchers. BCI has been proposed as assistive technology for individuals who are non-communicative or paralyzed, such as those with amyotrophic lateral sclerosis or spinal cord injury. The technology has also been suggested for enhancement and entertainment uses, and there are companies currently marketing BCI devices for those purposes as well as health-related purposes. The unprecedented direct connection created by BCI between human brains and computer hardware raises various (...) ethical, social, and legal challenges that merit further examination and discussion. To identify and characterize the key issues associated with BCI use, we performed a scoping review of biomedical ethics literature, analyzing the ethics concerns cited across multiple disciplines, including philosophy and medicine. Based on this investigation, we report that BCI research and its potential translation to therapeutic intervention generate significant ethical, legal, and social concerns, notably with regards to personhood, stigma, autonomy, privacy, research ethics, safety, responsibility, and justice. Our review of the literature determined, furthermore, that while these issues have been enumerated extensively, few concrete recommendations have been expressed. We conclude that future research should focus on remedying a lack of practical solutions to the ethical challenges of BCI, alongside the collection of empirical data on the perspectives of the public, BCI users, and BCI researchers. (shrink)

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. (shrink)

A brain-computer interface technology that can decode the neural signals associated with attempted but unarticulated speech could offer a future efficient means of communication for people with severe motor impairments. Recent demonstrations have validated this approach. Here we assume that it will be possible in future to decode imagined (i.e., attempted but unarticulated) speech in people with severe motor impairments, and we consider the characteristics that could maximize the social utility of a BCI for communication. As a social interaction, communication (...) involves the needs and goals of both speaker and listener, particularly in contexts that have significant potential consequences. We explore three high-consequence legal situations in which neurally-decoded speech could have implications:Testimony, where decoded speech is used as evidence;Consent and Capacity, where it may be used as a means of agency and participation such as consent to medical treatment; andHarm, where such communications may be networked or may cause harm to others. We then illustrate how design choices might impact the social and legal acceptability of these technologies. (shrink)

This study aims to identify artificial intelligence (AI) technologies that are applied in climate-smart agricultural practices and address ethical concerns of deploying those technologies from legal perspectives. As climate-smart agricultural AI, the study considers those AI-based technologies that are used for precision agriculture, monitoring peat lands, deforestation tracking, and improved forest management. The study utilized a systematic literature review approach to identify and analyze AI technologies employed in climate-smart agriculture and associated ethical and legal concerns. The study findings (...) indicate several ethical concerns for deploying AI in climate-smart agricultural practices pertaining to data inaccuracy, other technical errors based on wrong recommendations or wrongful acts, data ownership and intellectual property issues, and economic issues resulting in digital division and privacy and security related issues. In this study, the ethical concerns were further examined based on criminal law, tort law, privacy and data protection law, and intellectual property law. In this regard, the study finds that the current tort law pattern is more suitable than the criminal law pattern to address some major ethical concerns, such as data inaccuracy and other technical errors based on wrong recommendations or wrongful acts. Finally, the study recommends that at the global level, all countries need to fill up the current gap of international law on climate-smart agriculture through agreeing on a standard set of legal provisions and enhancing collaboration in innovation and deployment of climate-smart agricultural AI. It further recommends that at the local level, countries need to adopt suitable regulations addressing multi-stakeholders’ interests associated with the deployment of climate-smart agricultural AIs. (shrink)

An important challenge when creating automatically processable laws concerns open-textured terms. The ability to measure open-texture can assist in determining the feasibility of encoding regulation and where additional legal information is required to properly assess a legal issue or dispute. In this article, we propose a novel conceptualisation of open-texture with the aim of determining the extent of open-textured terms in legal documents. We conceptualise open-texture as a lever whose state is impacted by three types of forces: (...) internal forces (the words within the text themselves), external forces (the resources brought to challenge the definition of words), and lateral forces (the merit of such challenges). We tested part of this conceptualisation with 26 participants by investigating agreement in paired annotators. Five key findings emerged. First, agreement on which words are open-texture within a legal text is possible and statistically significant. Second, agreement is even high at an average inter-rater reliability of 0.7 (Cohen’s kappa). Third, when there is agreement on the words, agreement on the Open-Texture Value is high. Fourth, there is a dependence between the Open-Texture Value and reasons invoked behind open-texture. Fifth, involving only four annotators can yield similar results compared to involving twenty more when it comes to only flagging clauses containing open-texture. We conclude the article by discussing limitations of our experiment and which remaining questions in real life cases are still outstanding. (shrink)

Regulations affect every aspect of our lives. Compliance with the regulations impacts citizens and businesses similarly: they have to find their rights and obligations in the complex legal environment. The situation is more complex when languages and time versions of regulations should be considered. To propose a solution to these demands, we present a semantic enrichment approach which aims at (1) decreasing the ambiguousness of legal texts, (2) increasing the probability of finding the relevant legal materials, and (...) (3) utilizing the application of legal reasoners. Our approach is also implemented both as a service for citizens and businesses and as a modeling environment for legal drafters. To evaluate the usefulness of the approach, a case study was carried out in a large organization and applied to corporate regulations and Hungarian laws. The results suggest this approach can support the previous aims. (shrink)

We propose simple nonlinear mathematical models for the legal concept of balancing of interests. Our aim is to bridge the gap between an abstract formalisation of a balancing decision while assuring consistency and ultimately legal certainty across cases. We focus on the conflict between the rights to privacy and to the protection of personal data in Art. 7 and Art. 8 of the EU Charter of Fundamental Rights (EUCh) against the right of access to information derived from Art. (...) 11 EUCh. These competing rights are denoted by ( \(i_1\) ) _right to privacy _ and ( \(i_2\) ) _access to information_; mathematically, their indices are respectively assigned by \(u_1\in [0,1]\) and \(u_2\in [0,1]\) subject to the constraint \(u_1+u_2=1\). This constraint allows us to use one single index _u_ to resolve the conflict through balancing. The outcome will be concluded by comparing the index _u_ with a prior given threshold \(u_0\). For simplicity, we assume that the balancing depends on only selected legal criteria such as the social status of affected person, and the sphere from which the information originated, which are represented as inputs of the models, called legal parameters. Additionally, we take “time” into consideration as a legal criterion, building on the European Court of Justice’s ruling on the right to be forgotten: by considering time as a legal parameter, we model how the outcome of the balancing changes over the passage of time. To catch the dependence of the outcome _u_ by these criteria as legal parameters, data were created by a fully-qualified lawyer. By comparison to other approaches based on machine learning, especially neural networks, this approach requires significantly less data. This might come at the price of higher abstraction and simplification, but also provides for higher transparency and explainability. Two mathematical models for _u_, a time-independent model and a time-dependent model, are proposed, that are fitted by using the data. (shrink)

Brain–Computer Interface (BCI) technology is a promising research area in many domains. Brain activity can be interpreted through both invasive and non-invasive monitoring devices, allowing for novel, therapeutic solutions for individuals with disabilities and for other non-medical applications. However, a number of ethical issues have been identified from the use of BCI technology. In this paper, we review the academic discussion of the ethical implications of BCI technology in the last five years. We conclude that some emerging applications of BCI (...) technology—including commercial ventures that seek to meld human intelligence with AI—present new and unique ethical concerns. Further, we seek to understand how academic literature on the topic of BCIs addresses these novel concerns. Similar to prior work, we use a limited sample to identify trends and areas of concern or debate among researchers and ethicists. From our analysis, we identify two key areas of BCI ethics that warrant further research: the physical and psychological effects of BCI technology. Additionally, questions of BCI policy have not yet become a frequent point of discussion in the relevant literature on BCI ethics, and we argue this should be addressed in future work. We provide guiding questions that will help ethicists and policy makers grapple with the most important issues associated with BCI technology. (shrink)

Extractive summaries of legal judgments are invaluable due to their ability to preserve the original text, facilitating direct reference when required. Existing research on extractive summarization of Indian legal judgments predominantly employs supervised methodologies, which rely on extensive and often difficult-to-obtaining expert annotations. State-of-the-art methods that use role labels or rhetorical role labels are largely limited to supervised learning paradigms (Bhattacharya et al. in Identification of rhetorical roles of sentences in indian legal judgments, 2019). In this study, (...) we propose an unsupervised approach, Unsupervised Role-Labelled Knapsack Summarizer (_URL KnapSum_), which eliminates the need for expert annotations while effectively scaling to larger datasets. Our methodology begins by clustering sentences to group them into thematic categories. An optimized selection of sentences is performed using a knapsack algorithm using the similarity scores and length constraints, ensuring cluster-level diversity and global coherence. We evaluate the URL KnapSum using ROUGE (Lin in ROUGE: a package for automatic evaluation of summaries. In: Text summarization branches out, Barcelona, Spain. Association for Computational Linguistics, pp 74–81, 2004) scores for supervised evaluation and Kendall’s tau (Puka in Kendall’s Tau. Springer, Berlin, Heidelberg, pp 713–715, 2011) and Spearman’s correlation (Sedgwick in BMJ: Br Med J 349:g7327, 2014) for unsupervised evaluation. Additionally, we introduce a novel, reference-free evaluation metric, the _Top-K Analysis Metric_, which benchmarks the algorithm by measuring consistency between document and summary similarities. The results demonstrate the superiority of URL KnapSum over existing supervised methods, highlighting its effectiveness and scalability. (shrink)

Automatic summarization of legal case judgements, which are known to be long and complex, has traditionally been tried via extractive summarization models. In recent years, generative models including abstractive summarization models and Large language models (LLMs) have gained huge popularity. In this paper, we explore the applicability of such models for legal case judgement summarization. We applied various domain-specific abstractive summarization models and general-domain LLMs as well as extractive summarization models over two sets of legal case judgements (...) – from the United Kingdom (UK) Supreme Court and the Indian Supreme Court – and evaluated the quality of the generated summaries. We also perform experiments on a third dataset of legal documents of a different type – Government reports from the United States. Results show that abstractive summarization models and LLMs generally perform better than the extractive methods as per traditional metrics for evaluating summary quality. However, detailed investigation shows the presence of inconsistencies and hallucinations in the outputs of the generative models, and we explore ways to reduce the hallucinations and inconsistencies in the summaries. Overall, the investigation suggests that further improvements are needed to enhance the reliability of abstractive models and LLMs for legal case judgement summarization. At present, a human-in-the-loop technique is more suitable for performing manual checks to identify inconsistencies in the generated summaries. (shrink)

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 athttps://github.com/trusthlt/mining-legal-arguments. (shrink)

The focus of this book is on the epistemological and hermeneutic implications of data science and artificial intelligence for democracy and the Rule of Law. How do the normative effects of automated decision systems or the interventions of robotic fellow 'beings' compare to the legal effect of written and unwritten law? To investigate these questions the book brings together two disciplinary perspectives rarely combined within the framework of one volume. One starts from the perspective of 'code and law' and (...) the other develops from the domain of 'law and literature'. Integrating original analyses of relevant novels or films, the authors discuss how computational technologies challenge traditional forms of legal thought and affect the regulation of human behavior. Thus, pertinent questions are raised about the theoretical assumptions underlying both scientific and legal practice. (shrink)

The use of artificial intelligence (AI) to aid legal decision making has become prominent. This paper investigates the use of AI in a critical issue in employment law, the determination of a worker’s status—employee vs. independent contractor—in two common law countries (the U.S. and Canada). This legal question has been a contentious labor issue insofar as independent contractors are not eligible for the same benefits as employees. It has become an important societal issue due to the ubiquity of (...) the gig economy and the recent disruptions in employment arrangements. To address this problem, we collected, annotated, and structured the data for all Canadian and Californian court cases related to this legal question between 2002 and 2021, resulting in 538 Canadian cases and 217 U.S. cases. In contrast to legal literature focusing on complex and correlated characteristics of the employment relationship, our statistical analyses of the data show very strong correlations between the worker’s status and a small subset of quantifiable characteristics of the employment relationship. In fact, despite the variety of situations in the case law, we show that simple, off-the-shelf AI models classify the cases with an out-of-sample accuracy of more than 90%. Interestingly, the analysis of misclassified cases reveals consistent misclassification patterns by most algorithms. Legal analyses of these cases led us to identify how equity is ensured by judges in ambiguous situations. Finally, our findings have practical implications for access to legal advice and justice. We deployed our AI model via the open-access platform, https://MyOpenCourt.org/, to help users answer employment legal questions. This platform has already assisted many Canadian users, and we hope it will help democratize access to legal advice to large crowds. (shrink)

This book provides an overview of computer techniques and tools — especially from artificial intelligence (AI) — for handling legal evidence, police intelligence, crime analysis or detection, and forensic testing, with a sustained discussion of methods for the modelling of reasoning and forming an opinion about the evidence, methods for the modelling of argumentation, and computational approaches to dealing with legal, or any, narratives. By the 2000s, the modelling of reasoning on legal evidence has emerged as a (...) significant area within the well-established field of AI & Law. An overview such as this one has never been attempted before. It offers a panoramic view of topics, techniques and tools. It is more than a survey, as topic after topic, the reader can get a closer view of approaches and techniques. One aim is to introduce practitioners of AI to the modelling legal evidence. Another aim is to introduce legal professionals, as well as the more technically oriented among law enforcement professionals, or researchers in police science, to information technology resources from which their own respective field stands to benefit. Computer scientists must not blunder into design choices resulting in tools objectionable for legal professionals, so it is important to be aware of ongoing controversies. A survey is provided of argumentation tools or methods for reasoning about the evidence. Another class of tools considered here is intended to assist in organisational aspects of managing of the evidence. Moreover, tools appropriate for crime detection, intelligence, and investigation include tools based on link analysis and data mining. Concepts and techniques are introduced, along with case studies. So are areas in the forensic sciences. Special chapters are devoted to VIRTOPSY (a procedure for legal medicine) and FLINTS (a tool for the police). This is both an introductory book (possibly a textbook), and a reference for specialists from various quarters. (shrink)

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. (shrink)

Legal reasoning requires identification through search of authoritative legal texts (such as statutes, constitutions, or prior judicial opinions) that apply to a given legal question. In this paper, using a network representation of US Supreme Court opinions that integrates citation connectivity and topical similarity, we model the activity of law search as an organizing principle in the evolution of the corpus of legal texts. The network model and (parametrized) probabilistic search behavior generates a Pagerank-style ranking of (...) the texts that in turn gives rise to a natural geometry of the opinion corpus. This enables us to then measure the ways in which new judicial opinions affect the topography of the network and its future evolution. While we deploy it here on the US Supreme Court opinion corpus, there are obvious extensions to large evolving bodies of legal text (or text corpora in general). The model is a proxy for the way in which new opinions influence the search behavior of litigants and judges and thus affect the law. This type of “legal search effect” is a new legal consequence of research practice that has not been previously identified in jurisprudential thought and has never before been subject to empirical analysis. We quantitatively estimate the extent of this effect and find significant relationships between search-related network structures and propensity of future citation. This finding indicates that “search influence” is a pathway through which judicial opinions can affect future legal development. (shrink)

Computational models of relevance in case-based legal reasoning have traditionallybeen based on algorithms for comparing the facts and substantive legal issues of aprior case to those of a new case. In this paper we argue that robust models ofcase-based legal reasoning must also consider the broader social and jurisprudentialcontext in which legal precedents are decided. We analyze three aspects of legalcontext: the teleological relations that connect legal precedents to the socialvalues and policies they serve, (...) the temporal relations between prior andsubsequent cases in a legal domain, and the procedural posture of legal cases,which defines the scope of their precedential relevance. Using real examples drawnfrom appellate courts of New York and Massachusetts, we show with the courts' ownarguments that the doctrine of stare decisis (i.e., similar facts should lead to similar results) is subject to contextual constraints and influences. For each of the three aspects of legal context, we outline an expanded computational framework for case-based legal reasoning that encompasses the reasoning of the examples, and provides a foundation for generating a more robust set of legal arguments. (shrink)

The question as to whether or not an argument isadditional may be decisive in the evaluation ofjudicial decisions. It is, however, often difficult todistinguish between arguments that are additional(obiter dicta) and arguments that are not (ratio decidendi). This paper will focus on twoproblems concerning this distinction: thecharacterization of obiter dicta and theiridentification. A pragma-dialectical approach toargumentation will be the framework in which theseproblems will be addressed. Its insights intodialogical aspects of argumentation will be used tocharacterize obiter dicta. And its (...) assessment oflinguistic clues for the recognition of argumentationstructures will be used for the identification ofobiter dicta. Problems related to the identificationof obiter dicta will be illustrated through ananalysis of decisions in which the expressions anyway, for that matter and if onlybecause are considered as indicative of obiter dicta. (shrink)

Deep learning and other black-box models are becoming more and more popular today. Despite their high performance, they may not be accepted ethically or legally because of their lack of explainability. This paper presents the increasing number of legal requirements on machine learning model interpretability and explainability in the context of private and public decision making. It then explains how those legal requirements can be implemented into machine-learning models and concludes with a call for more inter-disciplinary research on (...) explainability. (shrink)

Bayesian approaches to legal reasoning propose causal models of the relation between evidence, the credibility of evidence, and ultimate hypotheses, or verdicts. They assume that legal reasoning is the process whereby one infers the posterior probability of a verdict based on observed evidence, or facts. In practice, legal reasoning does not operate quite that way. Legal reasoning is also an attempt at inferring applicable rules derived from legal precedents or statutes based on the facts at (...) hand. To make such an inference, legal reasoning follows syllogistic logic and first order transitivity. This paper proposes a Bayesian model of legal syllogistic reasoning that complements existing Bayesian models of legal reasoning using a Bayesian network whose variables correspond to legal precedents, statutes, and facts. We suggest that legal reasoning should be modelled as a process of finding the posterior probability of precedents and statutes based on available facts. (shrink)

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. (shrink)

The standard approach for abstractive text summarization is to use an encoder-decoder architecture. The encoder is responsible for capturing the general meaning from the source text, and the decoder is in charge of generating the final text summary. While this approach can compose summaries that resemble human writing, some may contain unrelated or unfaithful information. This problem is called “hallucination” and it represents a serious issue in legal texts as legal practitioners rely on these summaries when looking for (...) precedents, used to support legal arguments. Another concern is that legal documents tend to be very long and may not be fed entirely to the encoder. We propose our method called LegalSumm for addressing these issues by creating different “views” over the source text, training summarization models to generate independent versions of summaries, and applying entailment module to judge how faithful these candidate summaries are with respect to the source text. We show that the proposed approach can select candidate summaries that improve ROUGE scores in all metrics evaluated. (shrink)

There is a growing interest in how people conceptualise the legal domain for the purpose of legal knowledge systems. In this paper we discuss four such conceptualisations (referred to as ontologies): McCarty's language for legal discourse, Stamper's norma formalism, Valente's functional ontology of law, and the ontology of Van Kralingen and Visser. We present criteria for a comparison of the ontologies and discuss the strengths and weaknesses of the ontologies in relation to these criteria. Moreover, we critically (...) review the criteria. (shrink)

We propose the task of legal question generation (QG) as an application in Legal NLP. Specifically, the task is to generate a question, given a context and an optional keyword. We create the first dataset for the QG task in the legal domain, called LegalQ, consisting of 2023 pairs spanning the legal systems of multiple countries, and multiple languages. We then use this dataset to benchmark several Large Language Models (LLMs) including Turbo-GPT-3.5, GPT-4, Llama2-70b, Llama2-13b, and (...) Aalap-Mistral-7b (a legal domain-specific LLM). We also fine-tune several open-source LLMs such as T5, BART, Pegasus, and Flan-T5, which helps to improve results over zero-shot prompting of LLMs. We also use the idea of in-context learning (via few-shot examples) to generate questions of varying types and difficulty levels. Furthermore, we introduce a novel domain-specific prompting strategy based on chain-of-thoughts prompting for question generation. Further, we perform Bloom Taxonomy analysis of the questions generated by the LLMs, thereby showing that ‘understanding’ and ‘remembering’ are the two most dominant types of questions generated by the LLMs. Human evaluation of the generated questions shows promise in terms of generating grammatically correct, relevant, appropriate, complex, and novel questions. Finally, we analyze the incomplete or unanswerable generated questions to find possible reasons for these issues. (shrink)

Summarization of legal case judgement documents is a practical and challenging problem, for which many summarization algorithms of different varieties have been tried. In this work, rather than developing yet another summarization algorithm, we investigate if intelligently ensembling (combining) the outputs of multiple (base) summarization algorithms can lead to better summaries of legal case judgements than any of the base algorithms. Using two datasets of case judgement documents from the Indian Supreme Court, one with extractive gold standard summaries (...) and the other with abstractive gold standard summaries, we apply various ensembling techniques on summaries generated by a wide variety of summarization algorithms. The ensembling methods applied range from simple voting-based methods to ranking-based and graph-based ensembling methods. We show that many of our ensembling methods yield summaries that are better than the summaries produced by any of the individual base algorithms, in terms of ROUGE and METEOR scores. (shrink)

In this paper, we present an informal introduction to Logical English and illustrate its use to standardise the legal wording of the Automatic Early Termination clauses of International Swaps and Derivatives Association Agreements. LE can be viewed both as an alternative to conventional legal English for expressing legal documents, and as an alternative to conventional computer languages for automating legal documents. LE is a controlled natural language, which is designed both to be computer-executable and to be (...) readable by English speakers without special training. The basic form of LE is syntactic sugar for logic programs, in which all sentences have the same standard form, either as rules of the form conclusion if conditions or as unconditional sentences of the form conclusion. However, LE extends normal logic programming by introducing features that are present in other computer languages and other logics. These features include typed variables signalled by common nouns, and existentially quantified variables in the conclusions of sentences signalled by indefinite articles. Although LE translates naturally into a logic programming language such as Prolog or ASP, it can also serve as a neutral standard, which can be compiled into other lower-level computer languages. (shrink)

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. (shrink)

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. (shrink)

The task of rhetorical role labeling is to assign labels (such as Fact, Argument, Final Judgement, etc.) to sentences of a court case document. Rhetorical role labeling is an important problem in the field of Legal Analytics, since it can aid in various downstream tasks as well as enhances the readability of lengthy case documents. The task is challenging as case documents are highly various in structure and the rhetorical labels are often subjective. Previous works for automatic rhetorical role (...) identification (i) mainly used Conditional Random Fields over manually handcrafted features, and (ii) focused on certain law domains only (e.g., Immigration cases, Rent law), and a particular jurisdiction/country (e.g., US, Canada, India). In this work, we improve upon the prior works on rhetorical role identification by proposing novel Deep Learning models for automatically identifying rhetorical roles, which substantially outperform the prior methods. Additionally, we show the effectiveness of the proposed models over documents from five different law domains, and from two different jurisdictions—the Supreme Court of India and the Supreme Court of the UK. Through extensive experiments over different variations of the Deep Learning models, including Transformer models based on BERT and LegalBERT, we show the robustness of the methods for the task. We also perform an extensive inter-annotator study and analyse the agreement of the predictions of the proposed model with the annotations by domain experts. We find that some rhetorical labels are inherently hard/subjective and both law experts and neural models frequently get confused in predicting them correctly. (shrink)

This article describes recent jurisprudential accountsof analogical legal reasoning andcompares them in detail to the computational modelof case-based legal argument inCATO. The jurisprudential models provide a theoryof relevance based on low-levellegal principles generated in a process ofcase-comparing reflective adjustment. Thejurisprudential critique focuses on the problemsof assigning weights to competingprinciples and dealing with erroneously decidedprecedents. CATO, a computerizedinstructional environment, employs ArtificialIntelligence techniques to teach lawstudents how to make basic legal argumentswith cases. The computational modelhelps students test legal (...) hypotheses againsta database of legal cases, draws analogiesto problem scenarios from the database, andcomposes arguments by analogy with a setof argument moves. The CATO model accountsfor a number of the important featuresof the jurisprudential accounts, includingimplementing a kind of reflective adjustment.It also avoids some of the problems identifiedin the critique; for instance, it deals withweights in a non-numeric, context-sensitivemanner. The article concludes by describingthe contributions AI research can make tojurisprudential investigations of complexcognitive phenomena of legal reasoning. Forinstance, unlike the jurisprudential models,CATO provides a detailed account of how togenerate multiple interpretations of a citedcase, downplaying or emphasizing the legalsignificance of distinctions in terms of thepurposes of the law as the argument contextdemands. (shrink)

In this work we study, design, and evaluate computational methods to support interpretation of statutory terms. We propose a novel task of discovering sentences for argumentation about the meaning of statutory terms. The task models the analysis of past treatment of statutory terms, an exercise lawyers routinely perform using a combination of manual and computational approaches. We treat the discovery of sentences as a special case of ad hoc document retrieval. The specifics include retrieval of short texts, specialized document types, (...) and, above all, the unique definition of document relevance provided in detailed annotation guidelines. To support our experiments we assembled a data set comprising 42 queries which we plan to release to the public in the near future in order to support further research. Most importantly, we investigate the feasibility of developing a system that responds to a query with a list of sentences that mention the term in a way that is useful for understanding and elaborating its meaning. This is accomplished by a systematic assessment of different features that model the sentences’ usefulness for interpretation. We combine features into a compound measure that accounts for multiple aspects. The definition of the task, the assembly of the data set, and the detailed task analysis provide a solid foundation for employing a learning-to-rank approach. (shrink)

We define a formal semantics of conditionals based on _normatively ideal worlds_. Such worlds are described informally by Armgardt (Gabbay D, Magnani L, Park W, Pietarinen A-V (eds) Natural arguments: a tribute to john woods, College Publications, London, pp 699–708, 2018) to address well-known problems of the counterfactual approach to causation. Drawing on Armgardt’s proposal, we use iterated conditionals in order to analyse causal relations in scenarios of multi-agent interaction. This results in a refined counterfactual approach to causal responsibility in (...) legal contexts, which solves overdetermination problems in an intuitively accessible manner. (shrink)

Around the world, private and public organizations use software called legal expert systems to compute taxes. This software must comply with the laws they are designed to implement. As such, a bug or an error in a program that leads to tax miscalculations can have heavy legal and democratic consequences. However, increasing evidence suggests that some legal expert systems may not comply with the law. Moreover, traditional software development processes mean that legal expert systems are difficult (...) to adapt to the continuous flow of new legislation. To prevent further software decay and to reconcile these systems with the growing demand for algorithmic transparency, we argue that there is a need for a new development process for legal expert systems. This new system must be built to comply with the law, in particular the GDPR. It must also respect democratic transparency. For these reasons, we present a solution built by lawyers and computer scientists: C atala, a new programming language coupled with a pair programming development process. (shrink)

Modelling that exploits visual elements and information visualisation are important areas that have contributed immensely to understanding and the computerisation advancements in many domains and yet remain unexplored for the benefit of the law and legal practice. This paper investigates the challenge of modelling and expressing structures and processes in legislation and the law by using visual modelling and information visualisation (InfoVis) to assist accessibility of legal knowledge, practice and knowledge formalisation as a basis for legal AI. (...) The paper uses a subset of the well-defined Unified Modelling Language (UML) to visually express the structure and process of the legislation and the law to create visual flow diagrams called lawmaps, which form the basis of further formalisation. A lawmap development methodology is presented and evaluated by creating a set of lawmaps for the practice of conveyancing and the Landlords and Tenants Act 1954 of the United Kingdom. This paper is the first of a new breed of preliminary solutions capable of application across all aspects, from legislation to practice; and capable of accelerating development of legal AI. (shrink)

This study realizes belief/reliability change of a judge in a legal judgment by dynamic epistemic logic. A key feature of DEL is that possibilities in an agent’s belief can be represented by a Kripke model. This study addresses two difficulties in applying DEL to a legal case. First, since there are several methods for constructing a Kripke model, our question is how we can construct the model from a legal case. Second, since this study employs several dynamic (...) operators, our question is how we can decide which operators are to be applied for belief/reliability change of a judge. In order to solve these difficulties, we have implemented a computer system which provides two functions. First, the system can generate a Kripke model from a legal case. Second, the system provides an inconsistency solving algorithm which can automatically perform several operations in order to reduce the effort needed to decide which operators are to be applied. By our implementation, the above questions can be adequately solved. With our analysis method, six legal cases are analyzed to demonstrate our implementation. (shrink)

We present an annotation scheme describing the argument structure of judgement documents, a central construct in Japanese law. To support the final goal of this work, namely summarisation aimed at the legal professions, we have designed blueprint models of summaries of various granularities, and our annotation model in turn is fitted around the information needed for the summaries. In this paper we report results of a manual annotation study, showing that the annotation is stable. The annotated corpus we created (...) contains 89 documents. We also designed and implemented the first two stages of an algorithm for the automatic extraction of argument structure, and present evaluation results. (shrink)

Legal Judgment Summarization (LJS) can highly summarize legal judgment documents, improving judicial work efficiency in case retrieval and other occasions. Legal judgment documents are usually lengthy; however, most existing LJS methods are directly based on general text summarization models, which cannot handle long texts effectively. Additionally, due to the complex structural characteristics of legal judgment documents, some information may be lost by applying only one single kind of summarization model. To address these issues, we propose an (...) integrated summarization method which leverages both semantic and structural information to improve the quality of LJS. Specifically, legal judgment documents are firstly segmented into three relatively short parts according to their specific structure. We propose an extractive summarization model named BSLT and an abstractive summarization model named LPGN by adopting Lawformer as the encoder. Lawformer is a new pre-trained language model for long legal documents, which specializes in capturing long-distance dependency and modeling legal semantic features. Then, we adopt different models to summarize the corresponding part regarding its structural characteristics. Finally, the obtained summaries are integrated to generate a high-quality summary involving semantic and structural information. We conduct comparative experiments to evaluate the performance of our model. The results show that our model outperforms the baseline model LEAD-3 by 14.78% on the mean ROUGE score, which demonstrates our method is effective in LJS and is prospected to be applied to assist other tasks in legal artificial intelligence. (shrink)

Legal professionals in Malawi rely on a limited number of textbooks, outdated law reports and inadequate library services. Most documents available are in image form, are un-structured, i.e. contain no useful legal meta-data, summaries, keynotes, and do not support a system of citation that is essential to legal research. While advances in document processing and machine learning have benefited many fields, legal research is still only marginally affected. In this interdisciplinary research, the authors build semi-automatic tools (...) for creating a corpus of Malawi criminal law decisions annotated with legal meta-data, case and law citations. We used this corpus to extract legal meta-data, including law and case citations as used in Malawi by employing machine learning tools, spaCy and Gensim LDA. We set the foundation for a new methodology for classifying Malawi criminal case law according to the recently introduced International Classification of Crime for Statistical Purposes (ICCS). (shrink)

The subject of the research refers to the aspect of digital forensics and computer crime and the latter is one of the reasons for the evolution of crime in general. Based on the growing trend of technology development and the increase in the number of digital crimes, a special emphasis is given to the statistical aspect of computer crime as well as measures to reduce the impact of computer crime, including the ethical and legal aspects in the Republic (...) of North Macedonia (RNM). To conduct this research, statistical data on computer crime in RNM from 2011 to 2020 were used, based on historical data of analysis, as well as comparison, correlation, and the ANOVA-test. The highest number of crimes were registered in 2020 and a special feature is that a positive linear trend can be observed during the last decade. “Damage and unauthorized access to a computer system” took the first place in computer crimes according to the Criminal Procedure Law. (shrink)

The appropriate understanding and fast processing of lengthy legal documents are computationally challenging problems. Designing efficient automatic summarization techniques can potentially be the key to deal with such issues. Extractive summarization is one of the most popular approaches for forming summaries out of such lengthy documents, via the process of summary-relevant sentence selection. An efficient application of this approach involves appropriate scoring of sentences, which helps in the identification of more informative and essential sentences from the document. In this (...) work, a novel sentence scoring approach DCESumm is proposed which consists of supervised sentence-level summary relevance prediction, as well as unsupervised clustering-based document-level score enhancement. Experimental results on two legal document summarization datasets, BillSum and Forum of Information Retrieval Evaluation (FIRE), reveal that the proposed approach can achieve significant improvements over the current state-of-the-art approaches. More specifically it achieves ROUGE metric F1-score improvements of (1−6)% and (6−12)% for the BillSum and FIRE test sets respectively. Such impressive summarization results suggest the usefulness of the proposed approach in finding the gist of a lengthy legal document, thereby providing crucial assistance to legal practitioners. (shrink)

In an era characterized by fast technological progress that introduces new unpredictable scenarios every day, working in the law field may appear very difficult, if not supported by the right tools. In this respect, some systems based on Artificial Intelligence methods have been proposed in the literature, to support several tasks in the legal sector. Following this line of research, in this paper we propose a novel method, called PRILJ, that identifies paragraph regularities in legal case judgments, to (...) support legal experts during the redaction of legal documents. Methodologically, PRILJ adopts a two-step approach that first groups documents into clusters, according to their semantic content, and then identifies regularities in the paragraphs for each cluster. Embedding-based methods are adopted to properly represent documents and paragraphs into a semantic numerical feature space, and an Approximated Nearest Neighbor Search method is adopted to efficiently retrieve the most similar paragraphs with respect to the paragraphs of a document under preparation. Our extensive experimental evaluation, performed on a real-world dataset provided by EUR-Lex, proves the effectiveness and the efficiency of the proposed method. In particular, its ability of modeling different topics of legal documents, as well as of capturing the semantics of the textual content, appear very beneficial for the considered task, and make PRILJ very robust to the possible presence of noise in the data. (shrink)

This study examines the interplay between text summarization techniques and embeddings from Language Models (LMs) in constructing expert systems dedicated to the retrieval of legal precedents, with an emphasis on achieving cost-efficiency. Grounded in the growing domain of Artificial Intelligence (AI) in law, our research confronts the perennial challenges of computational resource optimization and the reliability of precedent identification. Through Named Entity Recognition (NER) and part-of-speech (POS) tagging, we juxtapose various summarization methods to distill legal documents into a (...) convenient form that retains their essence. We investigate the effectiveness of these methods in conjunction with state-of-the-art embeddings based on Large Language Models (LLMs), particularly ADA from OpenAI, which is trained on a wide range of general-purpose texts. Utilizing a dataset from one of Brazil’s administrative courts, we explore the efficacy of embeddings derived from a Transformer model tailored to legal corpora against those from ADA, gauging the impact of parameter size, training corpora, and context window on retrieving legal precedents. Our findings suggest that while the full text embedded with ADA’s extensive context window leads in retrieval performance, a balanced combination of POS-derived summaries and ADA embeddings presents a compelling trade-off between performance and resource expenditure, advocating for an efficient, scalable, intelligent system suitable for broad legal applications. This study contributes to the literature by delineating an optimal approach that harmonizes the dual imperatives of computational frugality and retrieval accuracy, propelling the legal field toward more strategic AI utilization. (shrink)

Few automated legal reasoning systems have been developed in domains of law in which a judicial decision maker has extensive discretion in the exercise of his or her powers. Discretionary domains challenge existing artificial intelligence paradigms because models of judicial reasoning are difficult, if not impossible to specify. We argue that judicial discretion adds to the characterisation of law as open textured in a way which has not been addressed by artificial intelligence and law researchers in depth. We demonstrate (...) that systems for reasoning with this form of open texture can be built by integrating rule sets with neural networks trained with data collected from standard past cases. The obstacles to this approach include difficulties in generating explanations once conclusions have been inferred, difficulties associated with the collection of sufficient data from past cases and difficulties associated with integrating two vastly different paradigms. A knowledge representation scheme based on the structure of arguments proposed by Toulmin has been used to overcome these obstacles. The system, known as Split Up, predicts judicial decisions in property proceedings within family law in Australia. Predictions from the system have been compared to those from a group of lawyers with favourable results. (shrink)

This article addresses the problem of automatically extracting legal indices which express the important contents of legal documents. Legal indices are not limited to single-word keywords and compound-word keywords, they are also clause keywords. We approach index extraction using structural information of Japanese sentences, i.e. chunks and clauses. Based on the assumption that legal indices are composed of important tokens from the documents, extracting legal indices is treated as a problem of collecting chunks and clauses (...) that contain as many important tokens as possible. Each token is assigned a weight which is a statistical score, e.g. TF–IDF and Okapi BM25, to indicate its importance. The importance of a chunk or clause is determined based on the average weight of tokens included in that chunk or clause. Then, highly weighted chunks and clauses are recognized as the indices for legal documents. The experimental results on Japanese National Pension Act data show that our proposed method achieves better performance than TextRank, the most popular unsupervised method in extracting single-word and compound-word keywords. In addition, this approach is also applicable to extract clause keywords with high performance. (shrink)

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. (shrink)

We show that the fundamental legal structure of a well-written financial contract follows a state-transition logic that can be formalized mathematically as a finite-state machine (specifically, a deterministic finite automaton or DFA). The automaton defines the states that a financial relationship can be in, such as “default,” “delinquency,” “performing,” etc., and it defines an “alphabet” of events that can trigger state transitions, such as “payment arrives,” “due date passes,” etc. The core of a contract describes the rules by which (...) different sequences of events trigger particular sequences of state transitions in the relationship between the counterparties. By conceptualizing and representing the legal structure of a contract in this way, we expose it to a range of powerful tools and results from the theory of computation. These allow, for example, automated reasoning to determine whether a contract is internally coherent and whether it is complete relative to a particular event alphabet. We illustrate the process by representing a simple loan agreement as an automaton. (shrink)

Legal document analysis presents significant challenges due to its complexity and domain-specific nature. This study introduces an innovative approach for classifying Indian court judgments into legal domains using diverse machine learning and deep learning techniques. The method incorporates feature engineering and deep learning algorithms for extracting meaningful features, supported by a wide range of classifiers, including voting classifiers, gradient boosting, and random forest. Embeddings are generated using models such as InLegalBERT, InCaseLawBERT, CustomInLawBERT, Mamba, T5, RoBERTa, CodeT5, SBERT, DistilBERT, (...) XLM, XLM Large, LegalBERT, GPT2, ALBERT, Electra, DeBERTa, TFDeBERTa, FlanT5, FlanT-Large, BART, BigBird Pegasus, LongFormer, and LUKE. To address class imbalance, the SMOTE technique is employed, and dimensionality is reduced using PCA and forward feature selection. The T5+SMOTE+feature selection+voting classifier configuration achieves a notable accuracy of 98%, highlighting the effectiveness of the proposed approach. These advancements have significant implications for applications such as document retrieval, legal discovery, and case law analysis, enhancing the accuracy and efficiency of legal document classification. (shrink)

Automated legal reasoning and its application in smart contracts and automated decisions are increasingly attracting interest. In this context, ethical and legal concerns make it necessary for automated reasoners to justify in human-understandable terms the advice given. Logic Programming, specially Answer Set Programming, has a rich semantics and has been used to very concisely express complex knowledge. However, modelling discretionality to act and other vague concepts such as ambiguity cannot be expressed in top-down execution models based on Prolog, (...) and in bottom-up execution models based on ASP the justifications are incomplete and/or not scalable. We propose to use s(CASP), a top-down execution model for predicate ASP, to model vague concepts following a set of patterns. We have implemented a framework, called s(LAW), to model, reason, and justify the applicable legislation and validate it by translating (and benchmarking) a representative use case, the criteria for the admission of students in the “Comunidad de Madrid”. (shrink)

This article seeks to address the problem of the ‘resource consumption bottleneck’ of creating legal semantic technologies manually. It describes a semantic role labeling based information extraction system to extract definitions and norms from legislation and represent them as structured norms in legal ontologies. The output is intended to help make laws more accessible, understandable, and searchable in a legal document management system.