This book explains China's intellectual property perspective in the context of European theories, through a critical examination of intellectual property theory and practice focused on China's compliance with the Agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS). The author's critical review of contemporary intellectual property philosophy suggests that justifying intellectual property protection through Locke or Hegel's property theories internalizes a theoretical paradox. "Professor Wenwei Guan's treatment of intellectual property law and practice in the (...) PRC offers new perspectives that enrich an already active field of study. This book will be a useful contribution to academic and policy discourses examining conceptual and operational dimensions of China's intellectual property protection system and the broader process of China's international engagement." - Dr. Pitman B. Potter, Professor of Law, University of British Columbia, Canada "Dr. Guan reminds us of the daunting challenge of the public-private divide in forming and reforming TRIPS regime; how this regime has failed to address development needs and public concerns in developing countries like China; and how TRIPS's 'birth defect' can be overcome and its evolution can be put back on the right track." - Dr. Yahong Li, Associate Professor at Faculty of Law, Hong Kong University. (shrink)

Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our knowledge, this is the first study proposing an advanced approach to law article prediction for the Italian legal system based on a BERT (Bidirectional Encoder Representations from Transformers) learning framework, which has (...) recently attracted increased attention among deep learning approaches, showing outstanding effectiveness in several natural language processing and learning tasks. We define LamBERTa models by fine-tuning an Italian pre-trained BERT on the Italian civil code or its portions, for law article retrieval as a classification task. One key aspect of our LamBERTa framework is that we conceived it to address an extreme classification scenario, which is characterized by a high number of classes, the few-shot learning problem, and the lack of test query benchmarks for Italian legal prediction tasks. To solve such issues, we define different methods for the unsupervised labeling of the law articles, which can in principle be applied to any law article code system. We provide insights into the explainability and interpretability of our LamBERTa models, and we present an extensive experimental analysis over query sets of different type, for single-label as well as multi-label evaluation tasks. Empirical evidence has shown the effectiveness of LamBERTa, and also its superiority against widely used deep-learning text classifiers and a few-shot learner conceived for an attribute-aware prediction task. (shrink)

Judgments concerning animals have arisen across a variety of established practice areas. There is, however, no publicly available repository of judgments concerning the emerging practice area of animal protection law. This has hindered the identification of individual animal protection law judgments and comprehension of the scale of animal protection law made by courts. Thus, we detail the creation of an initial animal protection law repository using natural language processing and machine learning techniques. This involved domain expert classification of 500 judgments (...) according to whether or not they were concerned with animal protection law. 400 of these judgments were used to train various models, each of which was used to predict the classification of the remaining 100 judgments. The predictions of each model were superior to a baseline measure intended to mimic current searching practice, with the best performing model being a support vector machine (SVM) approach that classified judgments according to term frequency—inverse document frequency (TF-IDF) values. Investigation of this model consisted of considering its most influential features and conducting an error analysis of all incorrectly predicted judgments. This showed the features indicative of animal protection law judgments to include terms such as ‘welfare’, ‘hunt’ and ‘cull’, and that incorrectly predicted judgments were often deemed marginal decisions by the domain expert. The TF-IDF SVM was then used to classify non-labelled judgments, resulting in an initial animal protection law repository. Inspection of this repository suggested that there were 175 animal protection judgments between January 2000 and December 2020 from the Privy Council, House of Lords, Supreme Court and upper England and Wales courts. (shrink)

This paper presents the first dataset for Japanese Legal Judgment Prediction (LJP), the Japanese Tort-case Dataset (JTD), which features two tasks: tort prediction and its rationale extraction. The rationale extraction task identifies the court’s accepting arguments from alleged arguments by plaintiffs and defendants, which is a novel task in the field. JTD is constructed based on annotated 3477 Japanese Civil Code judgments by 41 legal experts, resulting in 7978 instances with 59,697 of their alleged arguments from the involved parties. Our (...) baseline experiments show the feasibility of the proposed two tasks, and our error analysis by legal experts identifies sources of errors and suggests future directions of the LJP research. (shrink)

The social media industry lacks developed principles of professional ethics that it would need in order to better navigate the ethics of conforming to local media laws in authoritarian countries that lack meaningful protections for privacy, personal and political expression, and intellectual property. This chapter analyzes this question through three frameworks of professional ethics—journalism ethics, technology ethics, and business ethics—and the ways that social media resembles and crucially differs from these three industries.

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. (shrink)

Connected to the jurisprudence surrounding the copyrightability of a factual compilation, this book locates the footprints of the standard envisaged in a US Supreme court decision (Feist) in Europe. In particular, it observes the extent of similarity of such jurisprudence to the standard adopted and deliberated in the European Union. Many a times the reasons behind law making goes unnoticed. The compelling situations and the history existing prior to an enactment helps in understanding the balance that exists in a particular (...) legislation. While looking at the process of enacting the Database Directive (96/9/EC), this book reflects upon the concern that was expressed with the outcome of Feist decision in Europe. (shrink)

Einstein’s razor, a corollary of Ockham’s razor, is often paraphrased as follows: make everything as simple as possible, but not simpler. This rule of thumb describes the challenge that designers of a legal system face—to craft simple laws that produce desired ends, but not to pursue simplicity so far as to undermine those ends. Complexity, simplicity’s inverse, taxes cognition and increases the likelihood of suboptimal decisions. In addition, unnecessary legal complexity can drive a misallocation of human capital toward comprehending and (...) complying with legal rules and away from other productive ends. While many scholars have offered descriptive accounts or theoretical models of legal complexity, most empirical research to date has been limited to simple measures of size, such as the number of pages in a bill. No extant research rigorously applies a meaningful model to real data. As a consequence, we have no reliable means to determine whether a new bill, regulation, order, or precedent substantially effects legal complexity. In this paper, we begin to address this need by developing a proposed empirical framework for measuring relative legal complexity. This framework is based on “knowledge acquisition”, an approach at the intersection of psychology and computer science, which can take into account the structure, language, and interdependence of law. We then demonstrate the descriptive value of this framework by applying it to the U.S. Code’s Titles, scoring and ranking them by their relative complexity. We measure various features of a title including its structural size, the net flow of its intra-title citations and its linguistic entropy. Our framework is flexible, intuitive, and transparent, and we offer this approach as a first step in developing a practical methodology for assessing legal complexity. (shrink)

The study of law and information technology comes with an inherent contradiction in that while technology develops rapidly and embraces notions such as internationalization and globalization, traditional law, for the most part, can be slow to react to technological developments and is also predominantly confined to national borders. However, the notion of the rule of law defies the phenomenon of law being bound to national borders and enjoys global recognition. However, a serious threat to the rule of law is looming (...) in the form of an assault by technological developments within artificial intelligence (AI). As large strides are made in the academic discipline of AI, this technology is starting to make its way into digital decision-making systems and is in effect replacing human decision-makers. A prime example of this development is the use of AI to assist judges in making judicial decisions. However, in many circumstances this technology is a ‘black box’ due mainly to its complexity but also because it is protected by law. This lack of transparency and the diminished ability to understand the operation of these systems increasingly being used by the structures of governance is challenging traditional notions underpinning the rule of law. This is especially so in relation to concepts especially associated with the rule of law, such as transparency, fairness and explainability. This article examines the technology of AI in relation to the rule of law, highlighting the rule of law as a mechanism for human flourishing. It investigates the extent to which the rule of law is being diminished as AI is becoming entrenched within society and questions the extent to which it can survive in the technocratic society. (shrink)

This book provides international perspectives on the law of copyright in relation to three core themes - copyright and developing countries; the government and copyright; and technology and the future of copyright. The third theme includes an examination of the extent to which technology will dictate the development of the law, and a re-examination of the role of copyright in fostering innovation and creativity. As a critique, one chapter discusses how certain rights can create or reinforce social inequality under copyright (...) royalty systems. Underlying these themes is the role the law of copyright has in encouraging or impeding human flourishing. (shrink)

Disputes over causes play a central role in legal argumentation and liability attribution. Legal approaches to causation often struggle to capture cause-in-fact in complex situations, e.g. overdetermination, preemption, omission. In this paper, we first assess three current theories of causation to illustrate their strengths and weaknesses in capturing cause-in-fact. Secondly, we introduce a semi-formal framework for modelling causal arguments through strict and defeasible rules. Thirdly, the framework is applied to the Althen vaccine injury case. And lastly, we discuss the need (...) for new criteria based on a common causal argumentation framework and propose ideas on how to integrate the current theories of causation to assess the strength of causal arguments, while also acknowledging the tension between evidence-based and policy-based causal analysis in law. (shrink)

Disputes over causes play a central role in legal argumentation and liability attribution. Legal approaches to causation often struggle to capture cause-in-fact in complex situations, e.g. overdetermination, preemption, omission. In this paper, we first assess three current theories of causation to illustrate their strengths and weaknesses in capturing cause-in-fact. Secondly, we introduce a semi-formal framework for modelling causal arguments through strict and defeasible rules. Thirdly, the framework is applied to the Althen vaccine injury case. And lastly, we discuss the need (...) for new criteria based on a common causal argumentation framework and propose ideas on how to integrate the current theories of causation to assess the strength of causal arguments, while also acknowledging the tension between evidence-based and policy-based causal analysis in law. (shrink)

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper offers some commentaries on papers drawn from the Journal’s third decade. They indicate a major shift within Artificial Intelligence, both generally and in AI and Law: away from symbolic techniques to those based on Machine Learning approaches, especially those based on Natural Language texts rather than feature sets. Eight papers are discussed: two concern the management and use of documents available on the World Wide Web, (...) and six apply machine learning techniques to a variety of legal applications. (shrink)

Artificial intelligence plays an increasingly important role in legal disputes, influencing not only the reality outside the court but also the judicial decision-making process itself. While it is clear why judges may generally benefit from technology as a tool for reducing effort costs or increasing accuracy, the presence of technology in the judicial process may also affect the public perception of the courts. In particular, if individuals are averse to adjudication that involves a high degree of automation, particularly given fairness (...) concerns, then judicial technology may yield lower benefits than expected. However, the degree of aversion may well depend on how technology is used, i.e., on the timing and strength of judicial reliance on algorithms. Using an exploratory survey, we investigate whether the stage in which judges turn to algorithms for assistance matters for individual beliefs about the fairness of case outcomes. Specifically, we elicit beliefs about the use of algorithms in four different stages of adjudication: (i) information acquisition, (ii) information analysis, (iii) decision selection, and (iv) decision implementation. Our analysis indicates that individuals generally perceive the use of algorithms as fairer in the information acquisition stage than in other stages. However, individuals with a legal profession also perceive automation in the decision implementation stage as less fair compared to other individuals. Our findings, hence, suggest that individuals do care about how and when algorithms are used in the courts. (shrink)

Information technology is so ubiquitous and AI’s progress so inspiring that also legal professionals experience its benefits and have high expectations. At the same time, the powers of AI have been rising so strongly that it is no longer obvious that AI applications (whether in the law or elsewhere) help promoting a good society; in fact they are sometimes harmful. Hence many argue that safeguards are needed for AI to be trustworthy, social, responsible, humane, ethical. In short: AI should be (...) good for us. But how to establish proper safeguards for AI? One strong answer readily available is: consider the problems and solutions studied in AI & Law. AI & Law has worked on the design of social, explainable, responsible AI aligned with human values for decades already, AI & Law addresses the hardest problems across the breadth of AI (in reasoning, knowledge, learning and language), and AI & Law inspires new solutions (argumentation, schemes and norms, rules and cases, interpretation). It is argued that the study of AI as Law supports the development of an AI that is good for us, making AI & Law more relevant than ever. (shrink)

Disputes over causes play a central role in legal argumentation and liability attribution. Legal approaches to causation often struggle to capture cause-in-fact in complex situations, e.g. overdetermination, preemption, omission. In this paper, we first assess three current theories of causation to illustrate their strengths and weaknesses in capturing cause-in-fact. Secondly, we introduce a semi-formal framework for modelling causal arguments through strict and defeasible rules. Thirdly, the framework is applied to the Althen vaccine injury case. And lastly, we discuss the need (...) for new criteria based on a common causal argumentation framework and propose ideas on how to integrate the current theories of causation to assess the strength of causal arguments, while also acknowledging the tension between evidence-based and policy-based causal analysis in law. (shrink)

Law article prediction is a task of predicting the relevant laws and regulations involved in a case according to the description text of the case, and it has broad application prospects in improving judicial efficiency. In the existing research work, researchers often only consider a single case, employing the neural network method to extract features for prediction, which lack the mining of related and common element information between different data. In order to solve this problem, we propose a law article (...) prediction method that integrates the characteristics of common elements. It can effectively utilize the co-occurrence information of the training data, fully mine the relevant common elements between cases, and fuse local features. Experiments show that our method performs well. (shrink)

This paper connects to Jon Bing’s great vision of an integrated national legal information system. The intention of this paper is to variegate Bing’s vision of an integrated information system by shifting the focus to the lay users, thus to those, who are subject to the law. The modified vision is an integrated information system that supports intelligible access to law for the citizens. This presupposes however an unambiguous and transparent legal system. Accordingly, it is also stressed that intelligent legal (...) knowledge engineering has high potential to increase precision and rationality in law, and hence safeguard the predictability of law and legal certainty. The central hypothesis is that putting the person who is subject to the law at the centre provides more rational reason to aim for the greatest possible precision of law by exploiting the means and technologies of our time than to uphold strategic vagueness. On this basis, the paper proposes the development of a basic set of controllable and justiciable basic legal drafting standards on EU and on national level, and promotes a transparency by design approach for legal knowledge engineering in order to enhance precision, integrity, comprehensibility and accessibility of law. Similarly to Jon Bing’s vision, this modified vision of an integrated legal information systems is, most probably, also likely to remain unfulfilled. However, what would science be without visions and (science) fictions? (shrink)

The first issue of Artificial Intelligence and Law journal was published in 1992. This paper provides commentaries on nine significant papers drawn from the Journal’s second decade. Four of the papers relate to reasoning with legal cases, introducing contextual considerations, predicting outcomes on the basis of natural language descriptions of the cases, comparing different ways of representing cases, and formalising precedential reasoning. One introduces a method of analysing arguments that was to become very widely used in AI and Law, namely (...) argumentation schemes. Two relate to ontologies for the representation of legal concepts and two take advantage of the increasing availability of legal corpora in this decade, to automate document summarisation and for the mining of arguments. (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)

Legal Judgment Prediction (LJP) is a typical application of Artificial Intelligence in the intelligent judiciary. Current research primarily focuses on automatically predicting law articles, charges, and terms of penalty based on the fact description of cases. However, existing methods for LJP have limitations, such as neglecting document structure and ignoring case similarities. We propose a novel framework called Graph Contrastive Learning with Augmentation (GCLA) for legal judgment prediction to address these issues. GCLA constructs trainable document-level graphs for fact description, capturing (...) local and global context through sentence-level subgraphs. Graph augmentation enhances robustness. We introduce a comparison case relation perspective, using graph contrastive learning to model case-text label relationships effectively. Experimental results on real-world datasets demonstrate the competitive performance of GCLA. (shrink)

In this paper, we present a method for introducing law students to the writing of legal documents. The method uses a machine-readable representation of the legal knowledge to support document assembly and to help the students to understand how the assembly is performed. The knowledge base consists of enacted legislation, document templates, and assembly instructions. We propose a system called LEDAS (LEgal Document Assembly System) for the interactive assembly of legal documents. It guides users through the assembly process and provides (...) explanations of the interconnection between input data and claims stated in the document. The system acts as a platform for practicing drafting skills and has great potential as an education tool. It allows teachers to configure the system for the assembly of some particular type of legal document and then enables students to draft the documents by investigating which information is relevant for these documents and how the input data shape the final document. The generated legal document is complemented by a graphical representation of legal arguments expressed in the document. The system is based on existing legal standards to facilitate its introduction in the legal domain. Applicability of the system in the education of future lawyers is positively evaluated by the group of law students and their TA. (shrink)

The book in front of you is the first international academic volume on the legal, philosophical and economic aspects of the rise of 3D printing. In recent years 3D printing has become a hot topic. Some claim that it will revolutionize production and mass consumption, enabling consumers to print anything from clothing, automobile parts and guns to various foods, medication and spare parts for their home appliances. This may significantly reduce our environmental footprint, but also offers potential for innovation and (...) creativity. At the same time 3D printing raises social, ethical, regulatory and legal questions. If individuals can print anything they want, how does this affect existing systems of intellectual property rights? What are the societal consequences of the various types of products one can print with a 3D printer, for example weapons? Should all aspects of 3D printing be regulated, and if so, how and to what ends? How will businesses (have to) change their way of working and their revenue model in light of the shift to printing-on-demand? How will the role of product designers change in a world where everyone has the potential to design their own products? These and other questions are addressed in high quality and in-depth contributions by academics and experts, bringing together a wide variety of academic discussions on 3D printing from different disciplines as well as presenting new views, broadening the discussion beyond the merely technical dimension of 3D printing. Bibi van den Berg is Associate Professor at eLaw, the Center for Law and Digital Technologies at Leiden University, The Netherlands. Simone van der Hof is Full Professor at eLaw in Leiden and Eleni Kosta is Associate Professor at TILT, the Tilburg Institute for Law, Technology and Society at Tilburg University, The Netherlands. (shrink)

This paper shows that the law, in subtle ways, may set hitherto unrecognized incentives for the adoption of explainable machine learning applications. In doing so, we make two novel contributions. First, on the legal side, we show that to avoid liability, professional actors, such as doctors and managers, may soon be legally compelled to use explainable ML models. We argue that the importance of explainability reaches far beyond data protection law, and crucially influences questions of contractual and tort liability for (...) the use of ML models. To this effect, we conduct two legal case studies, in medical and corporate merger applications of ML. As a second contribution, we discuss the trade-off between accuracy and explainability and demonstrate the effect in a technical case study in the context of spam classification. (shrink)

This edited collection brings together a series of interdisciplinary contributions in the field of Information Technology Law. The topics addressed in this book cover a wide range of theoretical and practical legal issues that have been created by cutting-edge Internet technologies, primarily Big Data, the Internet of Things, and Cloud computing. Consideration is also given to more recent technological breakthroughs that are now used to assist, and - at times - substitute for, human work, such as automation, robots, sensors, and (...) algorithms. The chapters presented in this edition address these issues from the perspective of different legal backgrounds. The first part of the book discusses some of the shortcomings that have prompted legislators to carry out reforms with regard to privacy, data protection, and data security. Notably, some of the complexities and salient points with regard to the new European General Data Protection Regulation (EU GDPR) and the new amendments to the Japan's Personal Information Protection Act (PIPA) have been scrutinized. The second part looks at the vital role of Internet intermediaries (or brokers) for the proper functioning of the globalized electronic market and innovation technologies in general. The third part examines an electronic approach to evidence with an evaluation of how these technologies affect civil and criminal investigations. The authors also explore issues that have emerged in e-commerce, such as Bitcoin and its blockchain network effects. The book aims to explain, systemize and solve some of the lingering legal questions created by the disruptive technological change that characterizes the early twenty-first century. (shrink)

This book is about power and freedoms in our technological world and has two main objectives. The first is to demonstrate that a theoretical exploration of the algorithmic governmentality hypothesis combined with the capability approach is useful for a better understanding of power and freedoms in Ambient Intelligence, a world where information and communication technologies are invisible, interconnected, context aware, personalized, adaptive to humans and act autonomously. The second is to argue that these theories are useful for a better comprehension (...) of privacy and data protection concepts and the evolution of their regulation. Having these objectives in mind, the book outlines a number of theses based on two threads: first, the elimination of the social effects of uncertainty and the risks to freedoms and, second, the vindication of rights. Inspired by and building on the outcomes of different philosophical and legal approaches, this book embodies an effort to better understand the challenges posed by Ambient Intelligence technologies, opening paths for more effective realization of rights and rooting legal norms in the preservation of the potentiality of human capabilities. (shrink)

Intelligent Transportation Systems are expected to automate how parking slots are booked by trucks. The intrinsic dynamic nature of this problem, the need of explanations and the inclusion of private data justify an agent-based solution. Agents solving this problem act with a Believe Desire Intentions reasoning, and are implemented with JASON. Privacy of trucks becomes protected sharing a list of parkings ordered by preference. Furthermore, the process of assigning parking slots takes into account legal requirements on breaks and driving time (...) limits. Finally, the agent simulations use the distances, the number of trucks and parkings corresponding to the proportions of the current European Union data. The performance of the proposed solution is tested in these simulations with three different distances against an alternative with complete knowledge. The difference in efficiency, the number of illegal breaks and the traveled distances are measured in them. Comparing the results, we can conclude that the nonprivate alternative is slightly better in performance while both alternatives do not produce illegal breaks. In this way the simulations show that the proposed privacy protection does not impose a relevant handicap in efficiency. (shrink)

Judges in multiple US states, such as New York, Pennsylvania, Wisconsin, California, and Florida, receive a prediction of defendants’ recidivism risk, generated by the COMPAS algorithm. If judges act on these predictions, they implicitly delegate normative decisions to proprietary software, even beyond the previously documented race and age biases. Using the ProPublica dataset, we demonstrate that COMPAS predictions favor jailing over release. COMPAS is biased against defendants. We show that this bias can largely be removed. Our proposed correction increases overall (...) accuracy, and attenuates anti-black and anti-young bias. However, it also slightly increases the risk that defendants are released who commit a new crime before tried. We argue that this normative decision should not be buried in the code. The tradeoff between the interests of innocent defendants and of future victims should not only be made transparent. The algorithm should be changed such that the legislator and the courts do make this choice. (shrink)

We live in exciting times for AI and Law: technical developments are moving at a breakneck pace, and at the same time, the call for more robust AI governance and regulation grows stronger. How should we as an AI & Law community navigate these dramatic developments and claims? In this Presidential Address, I present my ideas for a way forward: researching, developing and evaluating real AI systems for the legal field with researchers from AI, Law and beyond. I will demonstrate (...) how we at the Netherlands National Police Lab AI are developing responsible AI by combining insights from different disciplines, and how this connects to the future of our field. (shrink)

The ubiquity of the Internet has given rise to new hybrid types of online users such as hybrid consumers and prosumers. This paper looks at some of the new legal challenges raised by the exciting opportunities for active participation and co-creation by such users in electronic commerce transactions. The method employed, in homage to Jon Bing, is to look back in time to understand how users in sales transactions have been progressively regarded—alternatively exposed to risk, alternatively protected—and how contract law (...) has adapted to such new developments. The paper shows that, even following the emergence of the hybrid consumer and the prosumer, there are strong arguments for the consumer’s continued protection, not least because of the increase in technological sophistication and complexity. The paper seeks to test somewhat the limits where consumer protection ends and where general contract law—with its attendant doctrines like caveat emptor—would apply again with respect to the aforementioned new users. (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)

Predictive judicial analytics holds the promise of increasing efficiency and fairness of law. Judicial analytics can assess extra-legal factors that influence decisions. Behavioral anomalies in judicial decision-making offer an intuitive understanding of feature relevance, which can then be used for debiasing the law. A conceptual distinction between inter-judge disparities in predictions and inter-judge disparities in prediction accuracy suggests another normatively relevant criterion with regards to fairness. Predictive analytics can also be used in the first step of causal inference, where the (...) features employed in the first step are exogenous to the case. Machine learning thus offers an approach to assess bias in the law and evaluate theories about the potential consequences of legal change. (shrink)

Artificial intelligence and related technologies are changing both the law and the legal profession. In particular, technological advances in fields ranging from machine learning to more advanced robots, including sensors, virtual realities, algorithms, bots, drones, self-driving cars, and more sophisticated "human-like" robots are creating new and previously unimagined challenges for regulators. These advances also give rise to new opportunities for legal professionals to make efficiency gains in the delivery of legal services. With the exponential growth of such technologies, radical disruption (...) seems likely to accelerate in the near future. This collection brings together a series of contributions by leading scholars in the newly emerging field of artificial intelligence, robotics, and the law. The aim of the book is to enrich legal debates on the social meaning and impact of this type of technology. The distinctive feature of the contributions presented in this edition is that they address the impact of these technological developments in a number of different fields of law and from the perspective of diverse jurisdictions. Moreover, the authors utilize insights from multiple related disciplines, in particular social theory and philosophy, in order to better understand and address the legal challenges created by AI. Therefore, the book will contribute to interdisciplinary debates on disruptive new AI technologies and the law. (shrink)

Building on the computer science concept of _code smells_, we initiate the study of _law smells_, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples—namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession—, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they (...) can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in practice using the United States Code as a test case. Our work demonstrates how ideas from software engineering can be leveraged to assess and improve the quality of _legal_ code, thus drawing attention to an understudied area in the intersection of law and computer science and highlighting the potential of computational legal drafting. (shrink)

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper discusses several topics that relate more naturally to groups of papers than a single paper published in the journal: ontologies, reasoning about evidence, the various contributions of Douglas Walton, and the practical application of the techniques of AI and Law.

This book examines the role of the EU in ensuring privacy and data protection on the internet. It describes and demonstrates the importance of privacy and data protection for our democracies and how the enjoyment of these rights is challenged by, particularly, big data and mass surveillance. The book takes the perspective of the EU mandate under Article 16 TFEU. It analyses the contributions of the specific actors and roles within the EU framework: the judiciary, the EU legislator, the independent (...) supervisory authorities, the cooperation mechanisms of these authorities, as well as the EU as actor in the external domain. Article 16 TFEU enables the Court of the Justice of the EU to play its role as constitutional court and to set high standards for fundamental rights protection. It obliges the European Parliament and the Council to lay down legislation that encompasses all processing of personal data. It confirms control by independent supervisory authorities as an essential element of data protection and it gives the EU a strong mandate to act in the global arena. The analysis shows that EU powers can be successfully used in a legitimate and effective manner and that this subject could be a success story for the EU, in times of widespread euroskepsis. It demonstrates that the Member States remain important players in ensuring privacy and data protection. In order to be a success story, the key stakeholders should be prepared to go the extra mile, so it is argued in the book. The book is based on academic research for which the author received a double doctorate at the University of Amsterdam and the Vrije Universiteit Brussels. It builds on a long inside experience within the European institutions, as well as within the community of data protection and data protection authorities. It is a must read in a time where the setting of EU privacy and data protection is changing dramatically, not only as a result of the rapidly evolving information society, but also because of important legal developments such as the entry into force of the General Data Protection Regulation. This book will appeal to all those who are in some way involved in making this regulation work. It will also appeal to people interested in the institutional framework of the European Union and in the role of the Union of promoting fundamental rights, also in the wider world.. (shrink)

We consider conditionals of the form A ⇒ B where A depends on the future and B on the present and past. We examine models for such conditional arising in Talmudic legal cases. We call such conditionals contrary to time conditionals.Three main aspects will be investigated: Inverse causality from future to past, where a future condition can influence a legal event in the past (this is a man made causality).Comparison with similar features in modern law.New types of temporal logics arising (...) from modelling the Talmudic examples. We shall see that we need a new temporal logic,which we call Talmudic temporal logic with linear open advancing future and parallel changing past, based on two parameters for time. (shrink)

This monograph poses a series of key problems of evidential reasoning and argumentation. It then offers solutions achieved by applying recently developed computational models of argumentation made available in artificial intelligence. Each problem is posed in such a way that the solution is easily understood. The book progresses from confronting these problems and offering solutions to them, building a useful general method for evaluating arguments along the way. It provides a hands-on survey explaining to the reader how to use current (...) argumentation methods and concepts that are increasingly being implemented in more precise ways for the application of software tools in computational argumentation systems. It shows how the use of these tools and methods requires a new approach to the concepts of knowledge and explanation suitable for diverse settings, such as issues of public safety and health, debate, legal argumentation, forensic evidence, science education, and the use of expert opinion evidence in personal and public deliberations. (shrink)

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. (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)

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)

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. (shrink)

Evidences are important proofs to support judicial trials. Automatically extracting evidences from judgement documents can be used to assess the trial quality and support “Intelligent Court”. Current evidence extraction is primarily depended on sequence labelling models. Despite their success, they can only assign a label to a token, which is difficult to recognize nested evidence entities in judgment documents, where a token may belong to several evidences at the same time. In this paper, we present a novel evidence extraction architecture (...) called ATT-MRC, in which extracting evidence entities is formalized as a question answer problem, where all evidence spans are screened out as possible correct answers. Furthermore, to address the data imbalance problem in the judgement documents, we revised the loss function and combined it with a data enhancement technique. Experimental results demonstrate that our model has better performance than related works in evidence extraction. (shrink)

Court judgments contain valuable information on how statutory laws and past court precedents are interpreted and how the interdependence structure among them evolves in the courtroom. Data-mining the evolving structure of such customs and norms that reflect myriad social values from a large-scale court judgment corpus is an essential task from both the academic and industrial perspectives. In this paper, using data from approximately 110,000 court judgments from Japan spanning the period 1998–2018 from the district to the supreme court level, (...) we propose two tasks that grasp such a structure from court judgments and highlight the strengths and weaknesses of major machine learning models. One is a prediction task based on masked language modeling that connects textual information to legal codes and past court precedents. Another is a dynamic link prediction task where we predict the hidden interdependence structure in the law. We make quantitative and qualitative comparisons among major machine learning models to obtain insights for future developments. (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)

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. (shrink)

In this paper, we discuss previous research in automatic prediction of court decisions. We define the difference between outcome identification, outcome-based judgement categorisation and outcome forecasting, and review how various studies fall into these categories. We discuss how important it is to understand the legal data that one works with in order to determine which task can be performed. Finally, we reflect on the needs of the legal discipline regarding the analysis of court judgements.

Increasing numbers of decisions about everyday life are made using algorithms. By algorithms we mean predictive models (decision rules) captured from historical data using data mining. Such models often decide prices we pay, select ads we see and news we read online, match job descriptions and candidate CVs, decide who gets a loan, who goes through an extra airport security check, or who gets released on parole. Yet growing evidence suggests that decision making by algorithms may discriminate people, even if (...) the computing process is fair and well-intentioned. This happens due to biased or non-representative learning data in combination with inadvertent modeling procedures. From the regulatory perspective there are two tendencies in relation to this issue: (1) to ensure that data-driven decision making is not discriminatory, and (2) to restrict overall collecting and storing of private data to a necessary minimum. This paper shows that from the computing perspective these two goals are contradictory. We demonstrate empirically and theoretically with standard regression models that in order to make sure that decision models are non-discriminatory, for instance, with respect to race, the sensitive racial information needs to be used in the model building process. Of course, after the model is ready, race should not be required as an input variable for decision making. From the regulatory perspective this has an important implication: collecting sensitive personal data is necessary in order to guarantee fairness of algorithms, and law making needs to find sensible ways to allow using such data in the modeling process. (shrink)