Legal Reasoning Models Research Paper

With the advent of computers and artificial intelligence, both the range of techniques available and the practical motivation for creating legal reasoning models have grown. Rule-based systems using logical inference techniques have been developed and used successfully in a number of legal domains, especially those dominated by complex regulations such as taxation and social benefits administration (Johnson and Mead 1991). A unique challenge in developing legal rule-based systems is the need to model the relationship between the logical representation and its textual sources (Bench-Capon and Coenen 1992). This need arises first, because legal experts (and legal expert systems) must be able to cite the authoritative texts on which their conclusions are based, and second, to enable legal knowledge bases to be efficiently updated when laws and regulations are amended.

Despite the successful adaptation of rule-based reasoning models to the legal domain, it is the view of most researchers that the essence of legal reasoning is its open-textured and indeterminate nature (Berman and Hafner 1987). Two essential cognitive abilities for legal reasoning models include:

(a) reasoning with cases to interpret open-textured or conflicting legal rules and concepts

(b) adversarial reasoning: the ability to create persuasive arguments for both sides of an issue.

2. Case-Based Legal Reasoning Models

Case-based reasoning (CBR) research in artificial intelligence is concerned with the use of remembered prior experiences in analyzing new situations and solving problems. The distinguishing characteristic of case-based reasoning is that prior experiences are stored as distinct histories or ‘cases,’ in contrast to other AI reasoning frameworks where knowledge (however acquired) is integrated into a unified structure of logical rules, frames, neural nets, or combinations of these.

Research in case-based legal reasoning has a more restricted meaning: formal judicial decisions (i.e., legal cases) play the role of remembered experiences, and the problem-solving goal is the application of a prior case to answer legal questions about a new case. In legal CBR models, a set of domain-dependent, legally-relevant features is defined, and a prior case is considered more or less on point depending on the degree of match between the features of the prior case and those of the new case. Computational models of legal CBR are concerned with the following problems: representation of cases, including their facts, issues, holdings (the judicial conclusions they contain), and results; organization of the case database, including the indexing structure and retrieval algorithms; ranking of retrieved cases by their similarity to a new case; and creation of case-based legal arguments.

The first legal CBR model that addressed all of these problems was embodied in HYPO (Rissland and Ashley 1987, Ashley 1990), a computer program for analyzing cases and constructing legal arguments in the domain of trade secrets law. In HYPO, legally relevant features called dimensions are used to index and retrieve cases. HYPO’s legal domain knowledge includes, for each feature, which side of a dispute would be favored by the feature’s having particular values (e.g., a true value for predicates, or a larger value for number-valued features), and how the feature’s value is related to a case’s underlying facts. For example, in the trade secret domain, one such feature is Generally-Known, indicating that the alleged secret information is generally known within the industry. When a new case is presented to HYPO, the features applicable to the new case are computed. Then, HYPO constructs a ‘claim lattice’—a similarity network that has the new case as its root node, and prior cases that share a maximal set of feature-values with the new case as its immediate successors. These immediate successors are taken to be the most-on-point cases.

Later research extended the use of HYPO-style CBR in several ways. In the GREBE system (Branting 1991), a representation of the ratio decidendi (the rationale or justification) of cases was used to identify portions of a prior case’s holdings that were relevant to a new case, even when other portions of the prior case were not on-point. Branting’s model addressed a well-known weakness of the pure feature-based approach to CBR: its failure to represent the reasoning that connects the features with the results of a case. The logic underlying a legal decision is implicitly encoded in GREBE by grouping the features of a precedent according to the holdings they influence. Berman and Hafner (1993) addressed another aspect of legal CBR: the need for legal reasoning models to include the societal goals and policies that motivate legal rules and decisions. They proposed an extended representation of cases which incorporates knowledge about legal purposes.

Several researchers have developed models of legal reasoning in which rules and cases are used in combination. Gardner (1987) used a logic tree to model the top-down, rule-based analysis of a legal situation. At the leaves of the tree are undefined concepts, linked to cases providing positive and negative examples. In the CABARET system, Skalak and Rissland (1991) described a mixed strategy for legal argumentation using both rules and cases. When a rule-based strategy produces a result favoring the opposing side, CABARET uses precedents to support argument moves such as the broadening of a rule’s interpretation or the discrediting of a rule.

3. Models Of Legal Argumentation

Argumentation is considered by many law teachers and legal scholars to be the quintessential element of a lawyer’s cognitive skill. Thus, many models of legal argumentation have been developed, falling into three broad categories: case-based, logic-based, and legal discourse models.

Case-based models of legal argumentation focus on the process of arguing with precedents, using techniques such as analogizing, distinguishing, and trumping. A case-based model of legal argumentation was developed as part of the HYPO system described above. In HYPO, after the claim lattice is created to determine the most-on-point precedents, HYPO constructs a three-ply argument for the new case. Letting Side 1 represent the party on whose behalf HYPO is arguing, first a Point for Side 1 is produced making a claim that Side 1 should win, next a Response for Side 2 is produced, countering the claim, and then a Rebuttal for Side 1 is produced to answer the response. To create the initial point for Side 1, HYPO identifies the most on point case whose result favored Side 1, and makes an argument by analogy, noting the similarity between the new case and the prior case. Responses and rebuttals use the legal argument techniques of distinguishing by focusing on differences rather than similarities, and trumping by presenting more on-point counterexamples (Ashley 1990, p. 71). Logic-based models of legal argumentation begin by expressing arguments as formal proofs within a system of logic, and then focus on the common situation in law where two logical arguments exist which reach contradictory results. In order to resolve such contradictions, it is necessary to adopt a framework of defeasible logic (Reiter 1980), where rules may have exceptions, and beliefs may be defeated by the acquisition of new, conflicting knowledge. For example, a legal rule ‘No vehicles are permitted in the park’ may co-exist with another legal rule ‘Emergency vehicles are permitted to violate traffic laws.’ Two logical arguments can then be constructed regarding an ambulance that entered the park: one concluding that the ambulance violated the law, the other concluding that the ambulance acted legally.

A logical argumentation framework is described in Prakken (1993), where two conflicting arguments, represented as formal proofs, can be compared using one or more ordering principles applied to the legal premises of the two arguments. (Traditional ordering principles found in legal texts include Lex Superior (e.g., federal laws trump state laws), Lex Specialis (more specific laws trump more general laws), and Lex posterior (newer laws trump older laws); however, Prakken’s framework allows for any criterion whatsoever that can prefer one argument to another.) Given a set of ordering principles, Prakken’s framework classifies legal arguments into three categories: justified arguments (which cannot be defeated), defeated arguments, and defensible arguments (which are neither justified nor defeated).

Legal discourse models are concerned with the structural and semantic characteristics that give a legal argument legitimacy. A number of argumentation models have been developed based on Toulmin’s (1958) theory of justified arguments. In Toulmin’s theory, an argument’s conclusion is called the claim; the grounds are the facts asserted to support the claim; the warrant is a bridge-like statement (e.g., a legal rule) that provides the logical connection between the grounds and the claim; and the backing is the authority for the warrant (e.g., a statute or regulation).

Loui and Norman (1995) have developed a discourse-based model of legal argumentation that focuses on defining categories of rationales (i.e., warrants) used in adversarial legal arguments, and the forms of attack appropriate for each. They identify several types of legal rationales: for example, the compression rationale justifies a simple and general rule as a normal-case simplification of a more complex and specific one. An example of a compression rationale described by Loui and Norman (p. 161) is the rule ‘vehicles are not allowed in the park,’ which (one might argue) is a normal-case simplification of the more complex rule: ‘vehicles used for private transportation are not allowed in the park,’ since normally vehicles are used for private transportation. A straightforward strategy for attacking the ‘no-vehicles’ rule (as in the case of the Memorial Day tank display) is to restate the rule in its uncompressed form, and argue that the case at hand falls outside the normal category of private transportation.

4. Conclusions And Future Directions

The field of law offers a fertile domain for the study of important problems in artificial intelligence and cognitive modeling, such as using prior experiences to solve new problems, reasoning with open-textured concepts, and making persuasive arguments. Promising new directions for research include the development of formal legal ontologies and their use in knowledge-based systems (Visser and Bench-Capon 1998) and the use of explicit legal concepts and models (such as contracts) to govern the behavior of systems for electronic commerce.

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