LLT Lab

    

Our empirical methodology leads to work products that make legal decision-making more accurate and more efficient. The application of logic to legal reasoning – especially to legal factfinding – enables us to represent knowledge in the legal domain and to create innovative, useful tools.

Rule Trees

We begin by extracting the legal rules governing the decisions we are investigating. Logically, a legal rule states the conditions under which a conclusion is true, and sets of related rules expand to form a “rule tree.” For example, in the Lab’s Vaccine/Injury Project, modeling a particular decision might require applying the statutory rules of the Vaccine Injury Compensation Program expanded by the case law established by Althen v. Secretary of Health and Human Services, 418 F.3d 1274 (Fed.Cir. 2005).

Reasoning Models for Legal Decisions

We model the factfinding or evidence assessment in a particular case by extending the branches of the rule tree to the evidence in the record, thus forming a reasoning model in the shape of a pyramid. For example, LLT researchers on the Vaccine/Injury Project have modeled the factfinding in Birdsell v. Secretary of the Department of Health and Human Services, No. 04-1755V, May 30, 2006.

Legal Apprentice™ Models

Software tailored to the Lab’s needs makes our work easier, and enables us to produce work products that are useful to others. The Legal Apprentice™ software designed by Apprentice Systems, Inc. creates a working environment in which we can encode the reasoning from a legal decision, assess the plausibility of the evidentiary assertions involved, and zip into a single file all documents important to the decision. You can download and open our Legal Apprentice™ reasoning model for the Birdsell case (mentioned above), to experience the working environment pictured below. Such technology also enables the Lab to create the kinds of work products described further below.

  

Useful Tools for Legal Practice

Our goal is to create work products and related tools that make legal decision-making more accurate and more efficient. Such products and tools can include:

• Searchable case files documenting the reasoning in decided cases, including the evidence assessment of the factfinder;


• Descriptions/models of reasoning patterns that have been successful or unsuccessful in past cases, which could enable parties in future cases to develop strategies for evidence production or to determine reasonable settlements;


• Critiques of reasoning patterns, and arguments for or against certain types of inferences in the face of uncertainties in the evidence; and


• Software tailored to assist factfinders and attorneys in organizing and assessing the evidence in particular cases, and to assist in coordinating teams of attorneys working on a single case.

Useful Tools for Legal Education

Our goal is to create work products and related tools that make legal education more effective, particularly in regard to training in logic skills. Such products and tools can include:

• Traditional educational materials on reasoning methods and on the substance of the law in research areas investigated by the Lab;


• Internet-based teaching materials suitable for traditional educational settings or for distance education, whether on logic skills generally or in particular substantive areas of the law; and


• Software that assists legal education in both traditional settings and continuing legal education.

Useful Tools for Legal Research

Our goal is to create work products and related tools that make legal research more penetrating and more useful. In addition to traditional law-review articles on legal methodology and on the substance of the law in targeted research areas, such products and tools can include:

• Reliable and valid methods for modeling the reasoning found in actual legal decisions, especially the evidence assessment of the factfinder (which traditional legal research seldom analyzes);


• Catalogs or databases of default-logic reasoning patterns found in legal documents, whether those patterns occur in the context of rule formulation, evidence assessment, or policy-based reasoning, and whether they are generic or specific to particular areas of law;


• Internet-based training materials for researchers, including guidance materials and illustrations of techniques of logic modeling;


• Software that automates interesting aspects of legal reasoning, and allows productive interchange with other research fields – including natural language processing (NLP), data mining (smart searches of legal databases), and artificial intelligence (AI); and


• Coordinated and comparative versions of all of the above, in collaboration with other research laboratories in the U.S. and elsewhere.