In October 2009, Professor Vern R. Walker became the first director of Hofstra Law’s newly founded Research Laboratory for Law, Logic and Technology (“LLT Lab”).
As stated in the Lab’s first Strategic Plan, the mission of the LLT Lab is “to conduct empirical research on substantive areas of law using a logic-based analytic framework and state-of-the-art technology, thereby creating knowledge, skills and tools useful in both legal practice and legal education.”
The vision of the LLT Lab is “to integrate state-of-the-art, empirical research on legal reasoning with the traditional educational activities of Hofstra Law School, and to create a valuable knowledge center for society through the collaborative efforts of students and faculty.”
Primary goals of the Lab are to create a legal research laboratory modeled on research laboratories in the sciences; to use teams of faculty and students to develop and disseminate practical tools for legal research, education and practice; to develop methodologies that enable the upward scaling of research projects by including additional researchers and collaborating legal research laboratories; and to use technology to produce useful knowledge and tools for society.
Just as science laboratories generate data by classifying and measuring real-world objects or events, the LLT Lab generates data by modeling the logical structure of the reasoning recorded in legal decisions.
The LLT Lab has two initial research projects. The first is the Vaccine/Injury Project (V/IP), in which the Lab studies proof of causation in vaccine compensation cases – that is, how to prove whether or not a vaccination caused a patient’s later injury or medical condition.
The second research project is the Comparative Medical Accident Liability Project (Comp-MAL), which is a joint research project with the International and Comparative Law Research Laboratory (“Lider Lab”) of the Scuola Superiore Sant’Anna in Pisa, Italy. Together, the two labs are conducting comparative investigations of medical malpractice decisions in the United States and Italy, looking for similarities and dissimilarities in the rule systems and proof patterns.
Researchers at the LLT Lab use special software called Legal Apprentice™ to create logic models of the fact-finding reasoning in the study decisions.
The software keeps track of the logic, and propagates plausibility-values and truth-values from individual items of evidence to the ultimate conclusion. The software also creates HTML documents of the logic models, as well as files of the models formatted in XML (a standard format used in Internet-based programs).
After creating accurate logic models of decided cases, LLT Lab researchers analyze patterns and trends within the data collected. Lab researchers identify, abstract and formalize the inference patterns that re-occur within the studied decisions.
The LLT Lab is especially interested in discovering “plausibility schemas,” which are patterns of reasoning that warrant default inferences to presumptively true conclusions. The research tries to identify which patterns the factfinders consider persuasive or not, and why. Because complete evidence is almost never available, this usually means developing “theories of uncertainty” – explanations about what evidence is missing, what uncertainty (potential for error) is inherent in drawing the conclusion, and how it could be reasonable to draw the conclusion even without the missing evidence.
The mission of the LLT Lab is not merely to study fact-finding using scientific methods, but also to improve actual decision making in society. The Lab uses its website (www.LLTLab.org) to make publicly available its database of logic models of decisions. Lab researchers also post commentary on those decisions in the form of blogs, as well as articles about patterns and trends they discover across multiple cases, and about broad aspects of the reasoning they study. A priority is developing and providing useful tools that will assist parties, attorneys and decision-makers in reaching accurate decisions more efficiently.
The LLT Lab’s approach to research and education has considerable potential as a paradigm. With respect to benefits to society generally, the goal is to produce databases of logic models for legal decisions in important social areas (such as vaccine-injury compensation), together with libraries of reasoning patterns that may be useful across many areas of law.
By making this research publicly available to all participants in the legal process, the LLT Lab’s work should increase the transparency and predictability of future decisions, and help ensure that similar cases will be decided similarly. Accuracy should increase as fact-finding reasoning is scrutinized. Moreover, decision-making processes should become more efficient because all participants will be able to better organize their evidence and better assess the settlement value of their cases. Finally, justice should increase because information and insights generated by the LLT Lab will be accessible to parties that could not otherwise afford such expensive and difficult research.
These benefits to society (increased transparency, predictability, accuracy, efficiency, and access to justice) should be achievable in many areas of the law, as work at the LLT Lab and other legal research labs progresses.
With respect to impact on research, the LLT Lab demonstrates how to apply scientific methods of modeling and measurement to legal reasoning, and especially to the reasoning of factfinders in actual cases.
The research develops libraries of plausibility schemas, or normative patterns of default reasoning, and tests important hypotheses about the structure and dynamics of fact finding. But in addition, the LLT Lab shows how the model of a research laboratory in the sciences can be applied in a legal setting, so that teams of students and faculty, employing tested methods of data gathering and analysis, can produce research that is valuable to society.
This work can also provide a paradigm for research in non-legal areas where documented decision-making is available.
The LLT Lab’s databases and pattern libraries should also provide valuable resources for research in related fields outside the law. The Lab’s modeling protocols and databases of analyzed legal decisions should provide resources for formal and informal logic theory, as well as for natural-language research in linguistics (especially semantics).
Moreover, the LLT Lab’s work should expand the empirical basis for research on artificial intelligence and law, particularly in the area of evidentiary reasoning, and the Lab’s modeling protocols should assist AI researchers in automating the extraction of reasoning from natural-language documents.
The subtleties of legal reasoning are difficult for non-lawyers to study, but the LLT Lab’s methodology makes the legal logic more accessible to non-lawyers.
With regard to the impact on education, the LLT Lab provides a unique paradigm for both legal education and for higher education generally.
The same techniques developed for analyzing the reasoning of a fact-finder will be useful in training students in logic and argumentation skills. The database of modeled cases provides numerous examples of evidentiary reasoning for students to study. Through the use of a team approach to research, the LLT Lab demonstrates how students can acquire logic skills in a research laboratory, while simultaneously producing important databases and tools for society.
As a result, the education process, in both law and elsewhere, might become more effective pedagogically, more engaging to students, and more productive for society.