Natural Language and Legal Tech: Could technology expand our understanding of jurisdiction?

Technology continues to transform the traditional legal landscape with new digital tools, making data-driven legal practice an increasingly powerful reality.  

Dr. Panezi’s research hypothesis is that Legal Tech tools—specifically natural language processing and machine learning—could be contributing to a modern phenomenon of tech-mediated transplants. As lawyers and judges increasingly benefit from developments in natural language processing and machine learning, are we witnessing the redefinition of language and jurisdictional boundaries? What is the long-term impact of these Legal Tech tools on jurisdictions?

While Legal Tech tools are new and innovative, legal transplants are far from a modern phenomenon. Looking into the past evolutions of laws before the introduction of Legal Tech tools can help us predict the future impact of these tools on the notion of jurisdiction. In fact, a glance at the past reveals that legal transplants—law imports and exports from different legal systems—influence each other.

Take the imperial and colonial exports of Dutch private law, for example, whose influence by imposition into jurisdictions like South Africa and Sri Lanka continued even after decolonization. Research shows that not only does the exporter influence a certain jurisdiction by imposing its own norms, but the exporter is also influenced in turn. The exporting jurisdiction finds itself adapting norms from the jurisdiction it is trying to influence, resulting in “mixed jurisdictions.”

Could the same be said for Legal Tech tools today? A jurisdiction’s natural language has an important role to play in the development of such tools. If the language of certain laws is shaping Legal Tech tools, it is plausible to think that the tools can then perhaps influence or reshape rules in other jurisdictions that ‘borrow’ or transplant the tools. Usually this will be jurisdictions that share a common language.

Imagining the long-term effects of these Legal Tech tools, we might envision a time in 50 years, argues Panezi, where such technologies have been so influential that they have begun to redefine language and jurisdictional barriers. Lawyers and judges may find themselves using algorithms that have been trained with data from different jurisdictions and in different languages. Could this become the norm for developing Legal Tech tools? Could legal results be products of the processing of a mixture of jurisdictions and laws? This is, of course, a hypothesis, that needs to be further tested.

Imagining the long-term effects of Legal Tech tools, we might envision a time in 50 years where such technologies have been so influential that they have begun to redefine language and jurisdictional barriers.

If this is the case, however, we need to ask whether our current linguistic and jurisdictional diversity is being reflected in our Legal Tech tools—and, currently, the answer seems to be no. While there exists large linguistic diversity in jurisdictions, it doesn’t appear to be reflected in Legal Tech markets. More dominant languages have more advanced Legal Tech markets—inevitably, perhaps, given that when you have a lot of data in one language for training algorithms, you can build and experiment much quicker.

While this has started from a speculative argument, it has major implications for what we predict the effects of Legal Tech to be on the future of law and our traditional understanding of jurisdiction. The development of Legal Tech tools is accelerating, especially solutions for services like contract reviews, litigation analytics, and brief drafting. All of these tools are being trained with laws that exist in real languages.

Thus, we need to explore whether we want more uniform or more diverse legal systems. Subsequently, should Legal Tech be trained in data from different jurisdictions? Answering these questions at an early stage will help us detect and manage tech-mediated ‘borrowing’—jurisdictional imports and exports—accordingly. This way, any transplants will actually be systematic or even planned. Otherwise, they remain random and de facto. Furthermore, we must reflect upon long-term targets when we think about the future of Legal Tech especially regarding the spectrum between jurisdictional uniformity and diversity. What are our benchmarks and targets? How will we measure the success of these legal transplants?

Exploring the questions surrounding Legal Tech, NLP, and our understanding of jurisdiction, Dr. Panezi is contributing to the work of IE Law School in pioneering approaches to Legal Tech and the field’s ongoing evolution and interaction with law practice today.

Dr. Argyri Panezi, Assistant Professor of Law and Technology at IE University, is an expert in law and technology and intellectual property. She specializes in Internet law and policy, intellectual property law, with an emphasis on digital copyright, as well as data protection, intellectual goods management, automation, machine learning and AI. Her current research focuses on digitization and AI.

Note: The views expressed by the author of this paper are completely personal and do not represent the position of any affiliated institution.