Models Of Legal Reasoning Research Articles

A Bayesian model of legal syllogistic reasoning

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.

Two factor-based models of precedential constraint: a comparison and proposal

The article considers two different interpretations of the reason model of precedent pioneered by John Horty. On a plausible interpretation of the reason model, past cases provide reasons to prioritize reasons favouring the same outcome as a past case over reasons favouring the opposing outcome. Here I consider the merits of this approach to the role of precedent in legal reasoning in comparison with a closely related view favoured by some legal theorists, according to which past cases provide reasons for undercutting (or ‘excluding’) reasons favouring the opposing outcome. After embedding both accounts within a general default logic, I note some important differences between the two approaches that emerge as a result of plausible distinctions between rebutting and undercutting defeat in formal models of legal reasoning. These differences stem from the ‘preference independence’ of undercutting defeat . Undercutting reasons succeed in defeating opposing reasons irrespective of their relative strength. As a result, the two accounts differ in their account of the way in which precedents constrain judicial reasoning. I conclude by suggesting that the two approaches can be integrated within a single model, in which the distinction between undercutting and rebutting defeat is used to account for the distinction between strict and persuasive forms of precedential constraint.

Transformative, troublesome and liminal: does the presentation of legal reasoning in the legal studies curricula in higher education institutions satisfy the characteristics of a threshold concept?

ABSTRACT A threshold concept is a crucial concept in learning whose defining characteristics are that it is transformative but sometimes troublesome to achieve. Legal reasoning has been viewed as a threshold concept in the legal studies curricula of higher education institutions which law students must master and, in some universities, is prescribed as a threshold graduate outcome for them to achieve. Referring to the formalistic model of legal reasoning, known as IRAC (issue, rule, application, conclusion) or similar acronyms, the question addressed in this paper is whether this method for legal reasoning is appropriate to lead towards the satisfaction of the characteristics of a threshold concept in legal studies. Through an in-depth examination of the literature on the three, primary characteristics that define a threshold concept: that it is “transformative, troublesome, and liminal”, it will be posited that use of the formalistic variant of IRAC is not suitable for the acquisition of legal reasoning skills. Rather, it is argued that a model underpinned by legal syllogism better aligns with these three characteristics of a threshold concept and with how law students might solve legal problems in the real world.

A Novel Understanding of Legal Syllogism as a Starting Point for better Legal Symbolic AI Systems

In the two most relevant categories of legal systems, the codified law systems and the case law systems, legal argumentation and legal reasoning are at the heart of legal theory and practice. Here we show that the concept of legal syllogism can be used as a starting point in both legal systems for new computational models of legal reasoning. We argue that legal syllogisms are not merely analytic logical inference rules but involve ampliative analogical inference. We aim to operationalize our account in terms of Context Graphs, i.e. internally consistent logical theories linked through rigorously defined analogical relations called views and argumentative relations such as attack and support.

'An Expansionist Model of Legal Reasoning'

In this paper, I engage with J.J. Moreso’s account of legal reasoning. First, I reconstruct Moreso’s conception of legal deliberation, which I regard as taking a distinctive stance within the contemporary debate in jurisprudence, since it innovatively expands on some of the most sophisticated models of practical reasoning and legal reasoning that have been defended in the literature. Building on this interpretation of Moreso’s theory of legal reasoning, I will go on presenting some reflections on the idea of practical rationality that undergirds the practice of reasoning in law, when this practice is conceived in Moreso’s terms. In this context, I account for two central features of the conception of practical rationality that underlines, and best fits with, Moreso’s explanation of legal reasoning. Since Moreso has yet to systematically explore the idea of practical rationality associated with his theory of legal argument, the claims I advance in this part of the paper are merely tentative. Whilst I try to make a rigorous attempt to develop Moreso’s jurisprudence from a theoretical perspective that is sympathetic to his overall project, nothing in the discussion I carry out here should be taken to suggest that Moreso is conceptually committed to the views about practical rationality I associate his theory of legal reasoning with.

세법상 법률 인공지능의 한계와 그 영향에 관한 試論的 考察

세법상 법률 인공지능의 한계와 그 영향에 관한 試論的 考察

Law is Inherently Normative, Not Moral: Hart, Dworkin, and the Rule-Following Paradox

What does it mean to give a legal interpretation? And, more specifically, when legal practitioners disagree about the proper resolution to a legal question, what are they actually arguing about? This paper creates a framework for answering those questions. In the first section, I develop a rudimentary model of legal reasoning: the act of legal interpretation involves selecting a set of mutually agreed upon 'axioms,' statements of legal rules which are clearly relevant to the situation at hand. These axioms are then aggregated into an overall legal rule which decides the issue in the case, in a process akin to logical derivation. In the remainder of this paper, I argue that this model is defective, and in ways which shed light on the concept of law debate which has descended from the Hart-Fuller and Hart-Dworkin exchanges. With reference to the rule interpretation problem discussed at length in Wittgenstein's Philosophical Investigations, I suggest that something 'more' must be added to the set of legal axioms applicable to the case in order to develop anything like a uniquely 'correct' outcome. This something, I argue, is an inherently normative standard -- one that provides an 'ought' under which rule candidates are to be evaluated. In the final section of the paper, I argue that this view is much like, but not identical to, Dworkinian constructive interpretation. I argue that a legal constructive interpretation is not, contra Dworkin, intrinsically 'moral' in any immediate sense. Rather, the normative standard to be used is that intrinsic to the act of lawmaking which produced the background legal propositions of importance. I then suggest that this understanding illustrates some conceptual problems for both the exclusive and inclusive legal positivist frameworks.

PENALARAN HUKUM YANG MAMPU MEWUJUDKAN TUJUAN HUKUM SECARA PROPORSIONAL

On the activity of legal reasoning, legal objective embodiment in the form of: justice, legal certainty, and kemanfaaan sometimes not achieved proportionately or even forgot or did not understand that the purpose of law should be the third contained proportionally in any legal ruling. The third objective will be achieved proportionately, by finding the point of the link between the reasoning of the law with the purpose of the law, the law of nature and the methods used to achieve the third objective of the law.The research was carried out using the method of legal research is research that is interdisciplinary collaboration and integration of the disciplines of law and other disciplines, which in this case is a philosophy. This research is a study of the legal reasoning of how capable of realizing justice, legal certainty and benefit proportionately. Therefore the examined was how the point links between legal reasoning with the purpose of the law, as well as what is the meaning of the law (the ontological aspect) and what is the method to achieve it (epistemological aspect).Research proves that there is a link between the point of law raised by Gustav Radbruch, namely: justice, legal certainty, and the benefit with the aksiologis of models of legal reasoning advanced by Shidarta. With regard to the activity of the legal reasoning that is capable of realizing justice, legal certainty, and benefit proportionally; found that all the arguments of yuridik which is a legal ruling made by establishing practical law was supposed to interpret the law as positive norms within the system of legislation that pay attention to the principles of truth and justice universal or moral, the behavioral pattern of the terlembaga, and the symbolic meanings of social actors.Whereas the epistemological aspects of the law which is its method is deductive reasoning patterns doctrinal-in order to be empowered in order to achieve legal certainty and fairness as well as simultaneously using inductive reasoning patterns nondoktrinal-so are empowered to achieve benefit. The pattern of reasoning is identical with the model of thought problematic tersistematisasi. Keywords: Law, Proportional Reasoning, the purpose of the law.

Legal analogy as an alternative to the deductive model of legal reasoning

This article demonstrates the inadequacy of legal deduction as a method that guarantees the certainty and predictability of law and its outcomes in concrete instances. Inter alia, the Author brings our attention to the far smaller role that the deductive pattern of inference plays in legal thought than one may suppose, since it is rather only a schematic illustration of the decisions that were previously made by recourse to the mental operations of a non-logical nature. In return, he proffers legal analogy as an alternative, by which he understands a mode of thinking which helps the reasoner to take into account a mass of different factors that are traditionally deemed to be relevant for legal thought and decision-making.

La coherencia en el Derecho

Este trabajo examina el concepto de coherencia y el papel que ésta juega en el razonamiento jurídico. En primer lugar, se ofrece un somero análisis de las teorías de la coherencia en el derecho, prestando atención a las teorías de la justificación tanto de los enunciados normativos como de los enunciados fácticos. En segundo lugar, se identifican una serie de problemas que afectan a las teorías de la coherencia en el derecho, tanto a las teorías que se ocupan de las cuestiones normativas como aquéllas que se centran en las cuestiones fácticas. En tercer lugar, se articula un modelo coherentista con el objetivo de solventar —o, al menos— mitigar los problemas que enfrentan las teorías coherentistas de la justificación jurídica. Según este modelo, una creencia acerca de los hechos o del derecho está justificada si y sólo si es óptimamente coherente, es decir, si es una creencia que un decisor jurídico epistémicamente responsable habría aceptado en virtud de su coherencia en circunstancias similares. Por último, se analizan las implicaciones del modelo de coherencia propuesto para una teoría general de la argumentación jurídica.

Building a Legal Expert System for Legal Reasoning in Specific Domain-A Survey

There is an urgent need to develop a legal expert system for thepeople to solve their legal troublesin order reduce problems to the people for frequently arising legal solutions or legal queries, there is a need of legal lawyer is mandatory, so there is an urgent need for the use of practical AI solutions in many of areas for problems which people encounter in their daily lives. In this context building a legal expert system for the people is most adequate to make legal decisions in a particular domain through which people can get basic service or suggestions or cou nseling or Fact finding and diagnosing few things associated to their domain. Such kind of emerging domain is calledImmovable Property Acquisition (IMP) like Tangible properties Land, House etc.., In order to build such kind of systems certain prior domai n knowledge must be acquired. The main fundamental goal of this paper is to survey distinguish prerequisites information about to make legal expert system. Here primarily focusing regarding Traditional model of legal reasoning, proposed approach by ExpertSystem, Various Logical components, Types of Legal Inference Techniques to automate the legal tasks.

La Coherencia en el Derecho

This paper examines the concept of coherence and its role in legal reasoning. First, it gives an overview of the coherence theory in law, paying attention to coherence theories of the justification of both normative and factual propositions. Second, it identifies some problem-areas confronting coherence theories of legal reasoning about both disputed questions of fact and disputed questions of law. Third, with a view to solving these problems, it proposes a coherence model of legal reasoning. The main tenet of this model is that a belief about the law and the facts under dispute is justified if it is 'optimally coherent,' that is, if it is such that an epistemically responsible legal decision-maker would have accepted it as justified by virtue of its coherence in like circumstances. Last, this paper examines the implications of the version of legal coherentism proposed for a general theory of legal reasoning and rationality.

Legal Justification by Optimal Coherence*

AbstractThis paper examines the concept of coherence and its role in legal reasoning. First, it identifies some problem areas confronting coherence theories of legal reasoning about both disputed questions of fact and disputed questions of law. Second, with a view to solving these problems, it proposes a coherence model of legal reasoning. The main tenet of this coherence model is that a belief about the law and the facts under dispute is justified if it is “optimally coherent,” that is, if it is such that an epistemically responsible legal decision‐maker would have accepted it as justified by virtue of its coherence in like circumstances. Last, looking beyond the coherence theory, the paper explores the implications of the version of legal coherentism proposed for a general theory of legal reasoning and rationality.

Building and Testing the SHYSTER-MYCIN Hybrid Legal Expert System

SHYSTER-MYCIN is a hybrid legal expert system created by combining rule-based and case-based reasoning. The MYCIN part uses a system of rules to reason with provisions of an Act of a parliament; the SHYSTER part uses analogy to reason with cases that explain open-textured concepts encountered in legislation.The construction of the expert system is focused upon: creating and evaluating a model of legal reasoning, and improving the reporting made by the MYCIN part.The model of legal reasoning is supported by jurisprudential discussion. The model holds that rules (in the strict sense of the word) cannot be extracted from cases. Cases should therefore be argued by analogy. The only rules that exist in law are those in legislation.The method of evaluating the model of legal reasoning is comparative. Reports by the system are compared with reports by a test group of legally trained people. Both the system and the test group were provided with the same material on which to base their reports. This ensured that the evaluation was of the model of reasoning, rather than the depth of knowledge.The reporting made by MYCIN was improved for use in SHYSTER-MYCIN, so that the system states how it comes to its conclusions. This reporting was then restricted to only the interesting conclusions.

Legal Expert Systems: The Inadequacy of a Rule-Based Approach

The two different categories of legal AI system are described, and legal analysis systems are chosen as objects of study. So-called judgment machines are discussed, but it is decided that research in legal AI systems would be best carried-out in the area of legal expert systems. A model of legal reasoning is adopted, and two different methods of legal knowledge representation are examined: rule-based systems and case-based systems. It is argued that a rule-based approach to legal expert systems is inadequate given the requirements of lawyers and the nature of legal reasoning about cases. A new, eclectic approach is proposed, incorporating both rule-based and case-based knowledge representation. It is claimed that such an approach can form the basis of an effective and useful legal expert system.

SHYSTER: A Pragmatic Legal Expert System

Most legal expert systems attempt to implement complex models of legal reasoning. Yet the utility of a legal expert system lies not in the extent to which it simulates a lawyer's approach to a legal problem, but in the quality of its predictions and of its arguments. A complex model of legal reasoning is not necessary: a successful legal expert system can be based upon a simplified model of legal reasoning. Some researchers have based their systems upon a jurisprudential approach to the law, yet lawyers are patently able to operate without any jurisprudential insight. A useful legal expert system should be capable of producing advice similar to that which one might get from a lawyer, so it should operate at the same pragmatic level of abstraction as does a lawyer-not at the more philosophical level of jurisprudence. A legal expert system called SHYSTER has been developed to demonstrate that a useful legal expert system can be based upon a pragmatic approach to the law. SHYSTER has a simple representation structure which simplifies the problem of knowledge acquisition. Yet this structure is complex enough for SHYSTER to produce useful advice. SHYSTER is a case-based legal expert system (although it has been designed so that it can be linked with a rule-based system to form a hybrid legal expert system). Its advice is based upon an examination of, and an argument about, the similarities and differences between cases. SHYSTER attempts to model the way in which lawyers argue with cases, but it does not attempt to model the way in which lawyers decide which cases to use in those arguments. Instead, it employs statistical techniques to quantify the similarity between cases. It decides which cases to use in argument, and what prediction it will make, on the basis of that similarity measure. SHYSTER is of a general design: it provides advice in areas of case law that have been specified by a legal expert using a specification language. Four different, and disparate, areas of law have been specified for SHYSTER, and its operation has been tested in each of those legal domains. Testing of SHYSTER in these four domains indicates that it is exceptionally good at predicting results, and fairly good at choosing cases with which to construct its arguments. SHYSTER demonstrates the viability of a pragmatic approach to legal expert system design.

Special Issue of the journal Artificial Intelligence on “AI & Law”

The field of AI & Law uses concepts and tools from artificial intelligence to investigate the law, with the twin goals of better understanding legal reasoning and building tools to support tasks in law. General topics addressed in AI & Law include: • investigation of legal reasoning and argumentation using computational methods; • applications of AI and advanced information technology to support legal practice and legal education; • development and exploration of techniques from AI using law as the example domain. For instance, AI & Law investigations of legal argumentation have employed techniques from the AI specialties of case-based reasoning, deontic logic, and nonmonotonic reasoning. Early investigations of legal argumentation contributed to the birth of the AI specialty of Case-Based Reasoning. Results of this work have been applied in intelligent tutoring systems that teach law students how to argue with cases. Recent work has tackled the problem of making retrieval of legal information more intelligent by combining AI techniques with those from information retrieval. Over the years, many projects have employed techniques from logic and rule-based reasoning to build computational models and practical tools for areas of administrative and regulatory law. Other projects have explored the very nature of legal rules. The legal domain has been used as an example for the study of concept drift, concept learning, and reasoning by example. The AI & Law community includes legal scholars, philosophers of jurisprudence, as well as AI researchers. A vibrant international community, it has been meeting on a regular basis since 1987, when the first International Conference on AI and Law was held in Boston. In this issue, we seek papers on a variety of topics that showcase the visions and accomplishments of the field of AI & Law. Authors are invited to submit papers on topics including but not restricted to: • computational models of legal reasoning and argumentation; • legal knowledge-based systems; • case-based legal reasoning;

Near Knowledge: Inductive Learning Systems in Law

Induction is an interesting model of legal reasoning, since it provides a method of capturing initial states of legal principles and rules, and adjusting these principles and rules over time as the law changes. In this Article I explain how Artificial Intelligence-based inductive learning algorithms work, and show how they have been used in law to model legal domains. I identify some problems with implementations undertaken in law to date, and create a taxonomy of appropriate cases to use in legal inductive inferencing systems. I suggest that inductive learning algorithms have potential in modeling law, but that the artificial intelligence implementations to date are problematic. I argue that induction should be further investigated, since it has the potential to be an extremely useful mechanism for understanding legal domains.

A Fuzzy Model of Legal Reasoning for Concurrent Engineering

The role of Concurrent Engineering is expanding to recognize enterprise issues in the product realization process. This work continues that trend by advocating proactive consideration of legal liability for defective design in preliminary design. To accomplish this, a fuzzy logic model of legal reasoning is demonstrated. The model evaluates the potential liability of a manufacturer for not adopting a pro posed alternative design. As such, it functions as a heuristic-based progressive model within Prasad's [24] Concurrent Engineering para digm. This model is based on the use of interpretive charts by the designer to quantitatively assess six variables on a zero to ten scale as inputs to the model. This allows evaluation of legal concepts by the fuzzy model, and it permits determination of a quantitative measure of potential legal liability for defective design. The system demonstrated here is validated using 100 cases, with acceptable responses at the 99% level. Additionally, use of this system is illustrated with four example cases for product design. Conclusions are drawn regarding poten tial use within the product realization process.

A theory of legal reasoning and a logic to match

This paper describes a model of legal reasoning and a logic for reasoning with rules, principles and goals that is especially suited to this model of legal reasoning. The paper consists of three parts. The first part describes a model of legal reasoning based on a two-layered view of the law. The first layer consists of principles and goals that express fundamental ideas of a legal system. The second layer contains legal rules which in a sense summarise the outcome of the interaction of the principles and goals for a number of case types. Both principles, goals and rules can be used in legal arguments, but their logical roles are different. One characteristic of the model of legal reasoning described in the first part of the paper is that it takes these logical differences into account. Another characteristic is that it pays serious attention to the phenomena of reasoning about the validity and acceptance of rules, respectively principles and goals, and about the application of legal rules, and the implications of these arguments for the use of rules, principles and goals in deriving legal conclusions for concrete cases. The second part of the paper first describes a logic (Reason-Based Logic) that is especially suited to deal with legal arguments as described in terms of the previously discussed model. The facilities of the logic are illustrated by means of examples that correspond to the several aspects of the model. The third part of the paper deals with a number of logico-philosophical reflections on Reason-Based Logic. The occasion is also used to compare these presuppositions with theories of defeasible reasoning based on the comparison of arguments.