Abstract
So far, in the application of legal analytics to legal sources, the substantive legal knowledge employed by computational models has had to be extracted manually from legal sources. This is the bottleneck, described in the literature. The paper is an exploration of this obstacle, with a focus on quantitative legal prediction. The authors review the most important studies about quantitative legal prediction published in recent years and systematize the issue by dividing them in text-based approaches, metadata-based approaches, and mixed approaches to prediction. Then, they focus on the main theoretical issues, such as the relationship between legal prediction and certainty of law, isomorphism, the interaction between textual sources, information, representation, and models. The metaphor of a crossroad shows a descriptive utility both for the aspects inside the bottleneck and, surprisingly, for the wider scenario. In order to have an impact on the legal profession, the test bench for legal quantitative prediction is the analysis of case law from the lower courts. Finally, the authors outline a possible development in the Artificial Intelligence (henceforth AI) applied to ordinary judicial activity, in general and especially in Italy, stressing the opportunity the huge amount of data accumulated before lower courts in the online trials offers.
Highlights
In the application of legal analytics to legal sources, the substantive legal knowledge employed by computational models has had to be extracted manually from legal sources
The bottleneck is described in very clear terms: “So far, the substantive legal knowledge employed by their computational models has had to be extracted manually from legal sources, that is, from the cases, statutes, regulations, contracts, and other texts that legal professionals use
This second part fragments into as many parts as are the natural languages used in the countries worldwide: This huge field is poorly explored and is waiting for the possibility offered by digital tools and big-data analytics
Summary
In his leading work on artificial intelligence and legal analytics, Kevin Ashley sharply states that “while AI and law researchers have made great strides, a knowledge representation bottleneck has impeded their progress toward contributing to legal practice” [1] (p. 29). The discovery of knowledge that can be found in text archives is the discovery of the many assumptions and fundamental ideas embedded in law and intertwined with legal theories. Those assumptions and ideas can be seen as roads crossing within the bottleneck.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have