Legal rules encompass not only codified statutes but also implicit adjudicatory principles derived from precedents that contain discretionary norms, social morality, and policy. While computational legal research has advanced in applying established rules to cases, inducing legal rules from judicial decisions remains understudied, constrained by limitations in model inference efficacy and symbolic reasoning capability. The advent of Large Language Models (LLMs) offers unprecedented opportunities for automating the extraction of such latent principles, yet progress is stymied by the absence of formal task definitions, benchmark datasets, and methodologies. To address this gap, we formalize Legal Rule Induction (LRI) as the task of deriving concise, generalizable doctrinal rules from sets of analogous precedents, distilling their shared preconditions, normative behaviors, and legal consequences. We introduce the first LRI benchmark, comprising 5,121 case sets (38,088 Chinese cases in total) for model tuning and 216 expert-annotated gold test sets. Experimental results reveal that: 1) State-of-the-art LLMs struggle with over-generalization and hallucination; 2) Training on our dataset markedly enhances LLMs capabilities in capturing nuanced rule patterns across similar cases.

Identifying regulatory statements in legislation is useful for developing metrics to measure the regulatory density and strictness of legislation. A computational method is valuable for scaling the identification of such statements from a growing body of EU legislation, constituting approximately 180,000 published legal acts between 1952 and 2023. Past work on extraction of these statements varies in the permissiveness of their definitions for what constitutes a regulatory statement. In this work, we provide a specific definition for our purposes based on the institutional grammar tool. We develop and compare two contrasting approaches for automatically identifying such statements in EU legislation, one based on dependency parsing, and the other on a transformer-based machine learning model. We found both approaches performed similarly well with accuracies of 80% and 84% respectively and a K alpha of 0.58. The high accuracies and not exceedingly high agreement suggests potential for combining strengths of both approaches.

Large Language Models (LLMs) could struggle to fully understand legal theories and perform complex legal reasoning tasks. In this study, we introduce a challenging task (confusing charge prediction) to better evaluate LLMs' understanding of legal theories and reasoning capabilities. We also propose a novel framework: Multi-Agent framework for improving complex Legal Reasoning capability (MALR). MALR employs non-parametric learning, encouraging LLMs to automatically decompose complex legal tasks and mimic human learning process to extract insights from legal rules, helping LLMs better understand legal theories and enhance their legal reasoning abilities. Extensive experiments on multiple real-world datasets demonstrate that the proposed framework effectively addresses complex reasoning issues in practical scenarios, paving the way for more reliable applications in the legal domain.

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".

Annually, e-commerce platforms incur substantial financial losses due to trademark infringements, making it crucial to identify and mitigate potential legal risks tied to merchant information registered to the platforms. However, the absence of high-quality datasets hampers research in this area. To address this gap, our study introduces TMID, a novel dataset to detect trademark infringement in merchant registrations. This is a real-world dataset sourced directly from Alipay, one of the world's largest e-commerce and digital payment platforms. As infringement detection is a legal reasoning task requiring an understanding of the contexts and legal rules, we offer a thorough collection of legal rules and merchant and trademark-related contextual information with annotations from legal experts. We ensure the data quality by performing an extensive statistical analysis. Furthermore, we conduct an empirical study on this dataset to highlight its value and the key challenges. Through this study, we aim to contribute valuable resources to advance research into legal compliance related to trademark infringement within the e-commerce sphere. The dataset is available at https://github.com/emnlpTMID/emnlpTMID.github.io .

In modern societies, law is one of the main tools to regulate human activities. These activities are constantly changing, and law co-evolves with them. In the past decades, human activities have become increasingly differentiated and intertwined, e.g., in developments described as globalization or digitization. Consequently, legal rules, too, have grown more complex, and statutes and regulations have increased in volume, interconnectivity, and hierarchical structure (Katz et al. 2020; Coupette et al. 2021a). A similar trend can be observed in software engineering, albeit on a much shorter time scale.

Until we make legal rules computational, we cannot make law computable. A big challenge for data science in law is capturing the governing legal rules in a computable format. They state the conditions under which laws are triggered, they identify the issues to be proved in a legal proceeding, and they structure the proof process itself. Where do we find them? How do they constrain legal reasoning?

Can computers read and apply legal rules? It's an idea that's gaining momentum, as it promises to make laws more accessible to the public and easier to follow. But it raises a host of legal, technical and ethical questions. The OECD recently published a white paper on "Rules as Code" efforts around the world. The Australian Senate Select Committee on Financial Technology and Regulatory Technology will be accepting submissions on the subject until 11 December 2020.

This paper examines and extends the legal microdirectives Recent research by legal scholars suggests that the law theories in three crucial respects: might inevitably be transformed into legal microdirectives consisting of legal rules that are derived (1) By indicating that legal micro-directives are from legal standards or that are otherwise produced likely to be AIenabled and evolve over time in automatically or via the consequent derivations of scope and velocity across the autonomous levels of legal goals and then propagated via automation for AI Legal Reasoning [20] [22], everyday use as readily accessible lawful directives throughout society. This paper examines and extends (2) By exploring the tradeoffs between legal the legal micro-directives theories in three crucial standards and legal rules as the imprinters of the respects: (1) By indicating that legal micro-directives micro-directives, and are likely to be AIenabled and evolve over time in scope and velocity across the autonomous levels of AI (3) By illuminating a set of brittleness exposures Legal Reasoning, (2) By exploring the tradeoffs that can undermine legal micro-directives and between legal standards and legal rules as the proffering potential mitigating remedies to seek imprinters of the micro-directives, and (3) By greater robustness in the instantiation and illuminating a set of brittleness exposures that can promulgation of such AIenabled lawful directives.

The use of Structured English as a computation independent knowledge representation format for non-technical users in business rules representation has been proposed in OMGs Semantics and Business Vocabulary Representation (SBVR). In the legal domain we face a similar problem. Formal representation languages, such as OASIS LegalRuleML and legal ontologies (LKIF, legal OWL2 ontologies etc.) support the technical knowledge engineer and the automated reasoning. But, they can be hardly used directly by the legal domain experts who do not have a computer science background. In this paper we adapt the SBVR Structured English approach for the legal domain and implement a proof-of-concept, called KR4IPLaw, which enables legal domain experts to represent their knowledge in Structured English in a computational independent and hence, for them, more usable way. The benefit of this approach is that the underlying pre-defined semantics of the Structured English approach makes transformations into formal languages such as OASIS LegalRuleML and OWL2 ontologies possible. We exemplify our approach in the domain of patent law.