The paper addresses the problem of making legal decisions about regulations such that they are compliant with legal principles. While decision-making using legal rules is well-developed in the literature, few researchers address reasoning with principles. A key difference between applying rules versus principles is that rules have a binary character, they are applied or not, while principles can be applied to a certain extent and in the case of conflicting principles, they require balancing instead of defeat. This paper provides a novel approach to balancing principles and deciding amongst regulations by proportional optimisation of values associated with regulations and principles. The approach can be generalised to other areas of decision-making.

Autonomous vehicles (AVs) must comply with regulatory frameworks to ensure road safety and predictability. Current proposed AV systems predominantly rely on machine learning models that lack explicit, computable representations of traffic laws, raising concerns about accountability and robustness in complex scenarios. This study proposes a novel pipeline that embeds formal logic rules in the autonomous agent to ensure legal compliance. To address the knowledge acquisition bottleneck, we propose using Large Language Models (LLMs), robust prompt engineering, and Logical English (LE) to translate traffic rules from natural language into a human-readable, executable rule-based framework. The pipeline includes an error correction phase to refine the process of extracting legal rules, which are then integrated into a simulation environment. Our approach successfully performed the translation of legal text into a structured, computable format, improving the transparency and interpretability of the high level decision making. The error correction phase improves rule accuracy, while simulations further validate rule compliance and performance in dynamic traffic scenarios.

Autonomous vehicles (AVs) must abide by the Highway Code. An AV agent would make plans which incorporate legal reasoning. Such plans must account for contingencies wherein the agent chooses between following a current plan that would lead to a violation of the law or providing an alternative plan which would not lead to a violation - a diversion. The paper utilises Defeasible Deontic Logic representing norms and encoded in Answer Set Programming (ASP) which is integrated with planning in ASP. The paper introduces diversions to address contingencies ith respect to legal reasoning.

In Value-based Argumentation Frameworks (VAFs), values are ascribed to abstract arguments and ordered one to another to reflect an audience’s preferences. An attack of one argument on another is successful only if the audience does not prefer the value of the attacked argument to the value of the attacking argument. Audiences can disagree about admissible arguments relative to their value preferences. Complementary to VAFs, this paper presents a novel integration of Value-Based Reasoning Frameworks (VBFs) with instantiated argumentation, specifically we focus on the structured argumentation approach of ASPIC+. Agents associate literals with social values and weight of values; together, these are used to filter the literals compatible with their values. Such a set of literals is used to construct agent-relative ASPIC+ knowledge bases, agent-relative instantiated arguments, and argumentation frameworks (AFs). Agents can attack one another’s arguments. VAF and VBF present complementary perspectives on values on arguments. VBF contributes a new, formal, articulated view of agreement and disagreement amongst agents, which is grounded in their values. In addition, VBF helps us understand how different agents choose what to argue from out of a pool of common resources.

Argumentation is often an attempt to resolve disagreement, but it is not always possible to reach a resolution. This is illustrated in law where multi-judge trials often end with a split decision. Not only do the judges disagree as to outcome (dissenting opinions), but also as to the reasons for a given outcome (concurring opinions). These disagreements can be explained in terms of different values held by the judges concerned. But while the role of values in determining which arguments are accepted has been widely explored, values can also determine which arguments can be constructed. The paper provides an analysis of this phenomenon.

Causal reasoning is a challenging topic not only in philosophy, science and in theories of human mind, but also in legal reasoning. Causality is indeed a key precondition for civil and criminal liability, in all cases dealing with the connection between human actions or omissions and harmful events. Only a partial overlap exists between natural causality (cause-in-fact) and legal causality: there are instances in which what appears to be a natural cause is not recognised as a legal one, as well as instances in which causality may be presumptively ascribed by the law in the absence of decisive evidence for natural causality. Legal policy considerations may explain these puzzling divergences, as we will discuss in the following. In this paper, we use argumentation schemes to provide simple and intuitive patterns for assessing causality in the legal domain. The analysis of these argument schemes will enable us to clarify some connections between natural and legal causation. Our schemes will include the necessary condition (but-for), overdetermination (NESS), preemption, presumptions based on the increase of risks or presumption based on statutory obligations, and interruption of causality due to unexpected events (Actus Novus). These approaches are tested on the basis of real legal cases in different domains.

Given a common pool of facts and legal rules, Judges on a panel may form different justifications for decisions, which are then voted upon. It is clear that a Judge’s personal values and purposes play in developing their opinion, which is a form of teleological reasoning. The paper introduces the Value-based Formal Reasoning (VFR) framework, which describes how a Judge’s personal values can be used in the construction of a justification for a decision.

Tools must be developed to help draft, consult, and explore textual legal sources. Between statistical information retrieval and the formalization of textual rules for automated legal reasoning, we defend a more pragmatic third way that enriches legal texts with a coarse-grained, interpretation-neutral, semantic annotation layer. The aim is that legal texts can be enriched on a large scale at a reasonable cost, paving the way for new search capabilities that will facilitate mining of legal sources. This new approach is illustrated on a proof-of-concept experiment that consisted in semantically annotating a significant part of the French version of the GDPR. The paper presents the design methodology of the annotation language, a first version of a Core Legal Annotation Language (CLAL), together with its formalization in XML, the gold standard resulting from the annotation of GDPR, and examples of user questions that can be better answered by semantic than by plain text search. This experimentation demonstrates the potential of the proposed approach and provides a basis for further development. All resources developed for that GDPR experiment are language independent and are publicly available.

In many legal disputes, determining and evaluating cause-in-fact is a crucial step in the liability attribution. It is, however, difficult and opaque. In this paper, we analyse the cases of overdetermination, where there is more than one cause for the outcome. The proposed framework (FCA) employs logic-based argument modelling. It distinguishes individual contributors in overdetermination cases by using a new set of critical questions based on argument schemes from effect-to-cause. To illustrate the use of the FCA, the Heneghan v Manchester Dry Docks lung cancer case with multi-party contributions is analysed.

Automatically understanding the rhetorical roles of sentences in a legal case judgement is an important problem to solve, since it can help in several downstream tasks like summarization of legal judgments, legal search, and so on. The task is challenging since legal case documents are usually not well-structured, and these rhetorical roles may be subjective (as evident from variation of opinions between legal experts). In this paper, we address this task for judgments from the Supreme Court of India. We label sentences in 50 documents using multiple human annotators, and perform an extensive analysis of the human-assigned labels. We also attempt automatic identification of the rhetorical roles of sentences. While prior approaches towards this task used Conditional Random Fields over manually handcrafted features, we explore the use of deep neural models which do not require hand-crafting of features. Experiments show that neural models perform much better in this task than baseline methods which use handcrafted features.

This paper presents a novel method to address legal rights for children through a chatbot framework by integrating machine learning, a dialogue graph, and information extraction. The method addresses a significant problem: we cannot presume that children have common knowledge about their rights or express themselves as an adult might. In our framework, a chatbot user begins a conversation, where based on the circumstance described, a neural network predicts both speech acts, relating to a dialogue graph, and legal types. Information is extracted in order to create a case for a legal advisor. In collaboration with the Children's Legal Centre Wales, who advocate for the improvement of legal rights in Wales, a corpus has been constructed and a prototype chatbot developed. The framework has been evaluated with classification measures and a user study.

LegalRuleML is a developing standard for representing the fine-grained semantic contents of legal texts. Such a representation would be highly useful for Semantic Web applications, but deriving formal rules from the textual source is problematic; there is currently little in the way of methodology to systematically transform language to LegalRuleML. To address this, we outline the purposes, processes, and outputs of a pilot study on the annotation of the contents of Scottish legal instruments, using key LegalRuleML elements as annotations. The resulting annotated corpus is assessed in terms of how well it answers the users' queries.

It is well recognised that it is difficult to make the semantic content of legal texts machine readable. We propose a systematic methodology to begin to render a sample legal text into LegalRuleML, which is a proposed markup for legal rules. We propose three levels – coarse, medium, and fine-grained analyses – each of which is compatible with LegalRuleML and which facilitate development from text to formal LegalRuleML. This paper provides guidelines for a coarse-grained analysis, highlighting some of the challenges to address even at this level.

Bar exams provide are a key watershed by which legal professionals demonstrate their knowledge of the law and its application. Passing the bar entitles one to practice the law in a given jurisdiction. The bar provides a excellent benchmark for the performance of legal information systems since passing the bar would arguably signal that the system has acquired key aspects of legal reason on a par with a human lawyer. The paper presents a first experiment with actual bar exam material, treating it as a form of textual entailment from the question to an answer. The Gold Standard is provided by the company which provides the bar exam. The paper reports a textual entailment student on a US bar exam, running ‘out of the box’ the Excitement Open Platform for textual entailment. The results and evaluation show that the tool can identify wrong answers (non-entailment) with a high F1 score, it performs poorly in identifying the correct answer (entailment). The results provide a baseline performance measure against which to evaluate future improvements. The reasons for the poor performance are examined, and proposals are made to augment the tool in the future.

This paper presents a semi-automatic methodology and application for identifying the Hohfeldian relation Duty in legal text, using the General Architecture for Text Engineering tool for the automated extraction of Duty instances and its associated roles. The method incrementally supports scholars in their interpretation.

In this article we offer a formal account of reasoning with legal cases in terms of argumentation schemes. These schemes, and undercutting attacks associated with them, are formalized as defeasible rules of inference within the ASPIC+ framework. We begin by modelling the style of reasoning with cases developed by Aleven and Ashley in the CATO project, which describes cases using factors, and then extend the account to accommodate the dimensions used in Rissland and Ashley's earlier HYPO project. Some additional scope for argumentation is then identified and formalized.

In this paper we present the motivation, use cases, design principles, abstract syntax, and initial core of LegalRuleML. The LegalRuleML-core is sufficiently rich for expressing legal sources, time, defeasibility, and deontic operators. An example is provided. LegalRuleMLis compared to related work.

Argument schemes can provide a means of explicitly describing reasoning methods in a form that lends itself to computation. The reasoning required to distinguish cases in the manner of CATO has been previously captured as a set of argument schemes. Here we present argument schemes that encapsulate another way of reasoning with cases: using preferences between social values revealed in past decisions to decide cases which have no exact matching precedents when the cases are described in terms of factors. We provide a set of schemes, with variations to capture different ways of comparing sets and varying degrees of promotion of values; we formalise these schemes; and we illustrate them with some examples.

Citizens have a variety of ways to consult with their representatives about policy proposals, seeking justifications, objecting to all or part of it, or making a counter-proposal. For the first, the representative needs only to state a justification. For the second, the representative would want to understand the objections, which may involve asking some questions. For the third, the citizen would have to provide a well formulated proposal that can then be critiqued from the standpoint of the government's own policy proposal. At the end of such a consultation, users will have aired their proposals, understood the implications, and received feedback on how their proposals contrast to that of the government.

In previous work we presented argumentation schemes to capture the CATO and value based theory construction approaches to reasoning with legal cases with factors. We formalised the schemes with ASPIC+, a formal representation of instantiated argumentation. In ASPIC+ the premises of a scheme may either be a factor provided in a knowledge base or established using a further argumentation scheme. Thus far we have taken the factors associated with cases to be given in the knowledge base. While this is adequate for expressing factor based reasoning, we can further investigate the justifications for the relationship between factors and facts or evidence. In this paper we examine how dimensions as used in the HYPO system can provide grounds on which to argue about which factors should apply to a case. By making this element of the reasoning explicit and subject to argument, we advance our overall account of reasoning with legal cases and make it more robust.

The paper reports the outcomes of a study with law school students to annotate a corpus of legal cases for a variety of annotation types, e.g. citation indices, legal facts, rationale, judgement, cause of action, and others. An online tool is used by a group of annotators that results in an annotated corpus. Differences amongst the annotations are curated, producing a gold standard corpus of annotated texts. The annotations can be extracted with semantic searches of complex queries. There would be many such uses for the development and analysis of such a corpus for both legal education and legal research.

Argumentation is key to understanding and evaluating many texts. The arguments in the texts must be identified; using current tools, this requires substantial work from human analysts. With a rule-based tool for semi-automatic text analysis support, we facilitate argument identification. The tool highlights potential argumentative sections of a text according to terms indicative of arguments (e.g. ‘suppose’ or ‘therefore’) and domain terminology (e.g. camera names and properties). The information can be used by an analyst to instantiate argumentation schemes and build arguments for and against a proposal. The resulting argumentation framework can then be passed to argument evaluation tools.

To make legal texts machine processable, the texts may be represented as linked documents, semantically tagged text, or translated to formal representations that can be automatically reasoned with. The paper considers the latter, which is key to testing consistency of laws, drawing inferences, and providing explanations relative to input. To translate laws to a form that can be reasoned with by a computer, sentences must be parsed and formally represented. The paper presents the state-of-the-art in automatic translation of law to a machine readable formal representation, provides corpora, outlines some key problems, and proposes tasks to address the problems.

Domain models have proven useful as the basis for the construction and evaluation of arguments to support deliberation about policy proposals. Using a model provides the means to systematically examine and understand the fine-grained objections that individuals might have about the policy. While in previous approaches, a justification for a policy proposal is presented for critique by the user, here, we reuse the domain model to invert the roles of the citizen and the Government: a policy proposal is elicited from the citizen, and a software agent automatically and systematically critiques it relative to the model and the Government's point of view. Such an approach engages citizens in a critical dialogue about the policy actions, which may lead to a better understanding of the implications of their proposals and that of the Government. A web-based tool that interactively leads users through the critique is presented.

Semantic models have received little attention in recent years, much of their role having been taken over by developments in ontologies. Ontologies, however, are static, and so have only a limited role in reasoning about domains in which change matters. In this paper, we focus on the domain of policy deliberation, where policy decisions are designed to change things to realise particular social values. We explore how a particular kind of state transition system can be constructed to serve as a semantic model to support reasoning about alternative policy decisions. The policy making process includes stages that support the construction of a model, which can then be exploited in reasoning. The reasoning itself will be driven by a particular argumentation scheme for practical reasoning, and the ways in which arguments based on this scheme can be attacked and evaluated. The evaluation provides alternative policy positions. The semantics underpin a current web-based implementation, designed to solicit structured feedback on policy proposals.

In this paper we offer an account of reasoning with legal cases in terms of argumentation schemes. These schemes, and undercutting attacks associated with them, are expressed as defeasible rules of inference that will lend themselves to formalisation within the AS-PIC+ framework. We begin by modelling the style of reasoning with cases developed by Aleven and Ashley in the CATO project, which describes cases using factors, and then extend the account to accommodate the dimensions used in Rissland and Ashley's earlier HYPO project. Some additional scope for argumentation is then identified and formalised.

The paper addresses the extraction, formalisation, and presentation of public policy arguments. Arguments are extracted from documents that comment on public policy proposals. Formalising the information from the arguments enables the construction of models and systematic analysis of the arguments. In addition, the arguments are represented in a form suitable for presentation in an online consultation tool. Thus, the forms in the consultation correlate with the formalisation and can be evaluated accordingly. The stages of the process are outlined with reference to a working example.

Rules in regulations such as found in the US Federal Code of Regulations can be expressed using conditional and deontic rules. Identifying and extracting such rules from the language of the source material would be useful for automating rulebook management and translating into an executable logic. The paper presents a linguistically-oriented, rule-based approach, which is in contrast to a machine learning approach. It outlines use cases, discusses the source materials, reviews the methodology, then provides initial results and future steps.

Legal case factors are textually represented facts which are represented in reported legal case decisions. Precedent decisions contribute to the decision of a case under consideration. As textually represented facts, factors linguistically encode semantic properties and relationships among the entities which can be leveraged to identify and extract the legal case factors from decisions. We integrate legal and linguistic resources in a text analysis tool with which we annotate textual passages. Using annotations tailored to legal case factors, the legal researcher can rapidly zero in on textual spans which represent specific combinations of factors, participants, and semantic properties which bear on who played what role with respect to a factor. The research reports progress on the development of a tool.

Much work using argumentation frameworks (AFs) treats arguments as entirely abstract, related by a uniform attack relation that always succeeds unless the attacker can itself be defeated. However, this does not seem adequate for legal argumentation. Some proposals have suggested regulating attack relations using preferences or values on arguments and that filter the attack relation, so that, depending on the audience addressed, some attacks fail and so can be removed from the framework. This does not, however, capture a central feature of legal reasoning: how a decision with respect to the same facts and legal reasoning varies as the judicial context varies. Nor does it capture related context-dependent features of legal reasoning, such as how an audience can prefer or value an argument, yet be constrained by precedent or authority not to accept it. Nor does it explain how certain types of attack may not be allowed in a particular procedural context. For this reason, evaluation of the status of arguments within a given framework must be allowed to depend not only on the attack relations along with the preference or value of arguments, but also on the nature of the attacks and the context in which they are made. We present a means to represent these features, enabling us to account for a number of factors currently considered to be beyond the remit of formal AFs. We give several examples of the use of approach including: appealing a case, overruling a precedent and rehearing of a case as a civil rather than criminal proceeding.