THE JURISPRUDENCE OF LEGAL KNOWLEDGE STRUCTURES

PRESENTATION ABSTRACT

November 16

This presentation is a portion of a much larger project on complexity theory and law. Complexity theory is an interdisciplinary field that studies the dynamic processes of variation and selection that are common in evolving systems. It has a significant impact on many areas of scientific inquiry, from physics to anthropology. In the social sciences, it investigates how social systems evolve. The application of complexity theory to law is a new field that is yielding insights into the communicative structures of social coordination. It challenges fundamental assumptions in epistemology, ontology, ethics, and democratic theory. So far, complexity theory has had little influence on legal philosophy, but it has proven valuable for legal text analysis and the prediction of legally significant outcomes. It would appear to be important for the future of jurisprudence, which seems likely to be far more computational in practice.

The issue considered here concerns how knowledge is represented (called “ontology”) in the emerging Web 3.0. There are two competing alternatives for legal ontologies: semantic or natural language. The semantic ontology is being developed in a system called the Standards Advancement of the Legal Industry (SALI), which has the support of the legal search industry. It is built by tagging texts and modeling the logic of grammar and syntax. It is similar to the project of the Logical Positivist philosopher Rudolf Carnap, who attempted to create a formal language that captured the logical structure of meaningful sentences. Carnap’s project is influential for many analytic jurisprudents. It was rejected by Willard Quine and (for different reasons) by Martin Heidegger. Quine's naturalized epistemology is influential for Brian Leiter's naturalism in jurisprudence, and Heidegger's critique of logical positivism is influential for critical legal theories. Both Quine and Heidegger agree that formal semantic languages obscure the subtle meanings that are essential to communicating the experience of moral meaning.

The alternative to semantic ontology is natural language ontology, which finds statistical correlations among words modeled as a complex network of significations. Here is where complexity theory comes in. Language is an evolving complex network that can be statistically modeled in a way that yields predictive analyses of natural language. It is a statistical model of language, not a logical model. The result is called a natural language ontology, which is used in products like Amazon’s Alexa, Apple’s Siri, and Open AI’s GPT-3. It is also used in legal products like e-discovery search engines. It has similarities to Hubert Dreyfus’ reading of Heidegger’s notion of “embodied” language in his book, What Computers Still Can’t Do.

In this presentation, I argue that a natural language ontology is the better alternative for Web 3.0 because it is more expressive of human moral experience and creates a more accessible structure for legal knowledge. The SALI ontology favors the commodification of legal knowledge and creates barriers to access. It empowers the status quo and may actually deepen the already abysmal lack of access to the legal system.