Computational models of legal precedent-based reasoning developed in AI and Law are typically based on the simplifying assumption that the background set of precedent cases is consistent. Besides being unrealistic in the legal domain, this assumption is problematic for recent promising applications of these models to the development of explainable AI methods. In this paper I explore a model of legal precedent-based reasoning that, unlike existing models, does not rely on the assumption that the background set of precedent cases is (...) consistent. The model is a generalization of the reason model of precedential constraint. I first show that the model supports an interesting deontic logic, where consistent obligations can be derived from inconsistent case bases. I then provide an explanation of this surprising result by proposing a reformulation of the model in terms of cases that support a new potential decision and cases that conflict with it. Finally, I show that the reformulation of the model allows us to verify that inconsistent case bases do not make verification that a decision is permissible substantially more complex than consistent case bases and to introduce intuitive criteria to compare different permissible decisions. (shrink)

The standard model of precedential constraint holds that a court is equally free to modify a precedent of its own and a precedent of a superior court—overruling aside, it does not differentiate horizontal and vertical precedents. This paper shows that no model can capture the U.S. doctrine of precedent without making that distinction. A precise model is then developed that does just that. This requires situating precedent cases in a formal representation of a hierarchical legal structure, and adjusting the constraint (...) that a precedent imposes based on the relationship of the precedent court and the instant court. The paper closes with suggestions for further improvements of the model. (shrink)

This paper is an introduction to a virtual special issue of AI and Law exploring the legacy of the influential HYPO system of Rissland and Ashley. The papers included are: Arguments and cases: An inevitable intertwining, BankXX: Supporting legal arguments through heuristic retrieval, Modelling reasoning with precedents in a formal dialogue Game, A note on dimensions and factors, An empirical investigation of reasoning with legal cases through theory construction and application, Automatically classifying case texts and predicting outcomes, A factor-based definition (...) of precedential constraint and An improved factor based approach to precedential constraint. After describing HYPO, in this introduction to the special issue I look at various aspects of its influence on AI and Law: the developments led by Rissland at Amherst; the developments led by Ashley in Pittsburgh; the expression of these ideas in terms of rule based systems, and their subsequent formalisation; value based theories, which were inspired by a critique of HYPO; and contemporary approaches which revive the idea of dimensions. (shrink)

This paper gives an account of dimensions in the reason model found in Horty : 1–33, 2011), Horty and Bench-Capon and Rigoni :133–160, 2015. doi: 10.1007/s10506-015-9166-x). The account is constructed with the purpose of rectifying problems with the approach to incorporating dimensions in Horty, namely, the problems arising from the collapse of the distinction between the reason model and the result model on that approach. Examination of the newly constructed theory revealed that the importance of dimensions in the reason model (...) lies in their ability to establish weighings between reasons/factors of the same polarity. This permits past cases to constrain future cases in ways they could not on the reason model with just factors. The paper then discusses how dimensions might be established from a case base and how dimensions that are incomplete in various aspects might be dealt with in the theory. It closes with comparisons to other work in the literature on AI and Law and suggestions for further improvement of the reason model. (shrink)

This article discusses the desirability and feasibility of modeling precedents with multiple interpretations within factor-based models of precedential constraint. The main idea is that allowing multiple reasonable interpretations of cases and modeling precedential constraint as a function of what all reasonable interpretations compel may be advantageous. The article explains the potential benefits of extending the models in this way with a focus on incorporating a theory of vertical precedent in U.S. federal appellate courts. It also considers the costs of extending (...) the models in this way, such as the significant increase in the functional size of the case base and the need to provide some kind of ordering on interpretations to select a “best” interpretation. Finally, the article suggests partially incorporating multiple interpretations of dimensions as a realistic starting point for incorporating interpretations generally, and shows how doing so can help address difficulties with dimensions. The conclusion remarks on the use of interpretations to deal with inconsistent precedents. (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)

This paper investigates the process of reasoning through which a judge determines whether a precedent-case gives her a binding reason to follow in her present-case. I review the objections that have been raised against the two main accounts of reasoning by precedent: the rule-account and the analogy-account. I argue that both accounts can be made viable by amending them to meet the objections. Nonetheless, I believe that there is an argument for preferring accounts that integrate analogical reasoning: any account of (...) reasoning by precedent that is descriptively minimally adequate will leave some room for judicial discretion. Discretion should be used under consideration of the best legally relevant arguments for and against a decision. Integrating analogical reasoning helps the judge to bring to her own attention the strongest case for following. Analogical reasoning also eases the recognition of possible reasons for distinguishing. Thereby, it facilitates a more balanced decision-making process. (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)

In this paper several recent factor- and dimension-based models of precedential constraint are formally investigated and an alternative dimension-based model is proposed. Simple factor- and dimension-based syntactic criteria are identified for checking whether a decision in a new case is forced, in terms of the relevant differences between a precedent and a new case, and the difference between absence of factors and negated factors in factor-based models is investigated. Then Horty’s and Rigoni’s recent dimension-based models of precedential constraint are critically (...) examined. An alternative to their reason models is proposed which is less expressive but arguably easier to apply in practice. (shrink)

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. (shrink)

The first issue of _Artificial Intelligence and Law_ journal was published in 1992. This paper provides commentaries on landmark papers from the first decade of that journal. The topics discussed include reasoning with cases, argumentation, normative reasoning, dialogue, representing legal knowledge and neural networks.

There are cases in which the literal interpretation of statutes may lead to counterintuitive consequences. When such cases go to high courts, judges may handle these counterintuitive consequences by identifying problematic rule conditions. Given that the law consists of a large number of rule conditions, it is demanding and exhaustive to figure out which condition is problematic. For solving this problem, our work aims to assist judges in civil law systems to resolve counterintuitive consequences using logic program representation of statutes (...) and Legal Debugging. The core principle of Legal Debugging is to cooperate with a user to find a culprit, a root cause of counterintuitive consequences. This article proposes an algorithm to resolve a culprit. Since the statutes are represented by logic rules but changes in law are initiated by cases, we adopt a prototypical case with judgement specified by a set of rules. Then, to resolve a culprit, we reconstruct a program so that it provides reasons as if we applied case-based reasoning to a new set of prototypical cases with judgement, which include a new set of facts relevant to a considering case. (shrink)