The Promise of AI in an Open Justice System

Abstract

To craft effective public policy, modern governments must gather and analyze data on both the performance of their public functions and the responses by the public. Federal administrative agencies such as the Patent Office and Centers for Disease Control routinely do this, as does the United States Congress. More importantly, they make such data freely accessible. Within the United States government, however, the judicial branch is a conspicuous outlier. In theory, federal court records could be used to evaluate the efficiency and fairness of the justice system. In practice, court records are effectively out of reach because they sit behind a government paywall. This financial barrier, along with an equally important myriad of technical obstacles, have forestalled the development of AI‐driven analysis that could enable a systematic understanding and evaluation of the work of the courts. The Systematic Content Analysis of Litigation EventS Open Knowledge Network (SCALES OKN) seeks to address this situation by transforming the transparency and accessibility of court records. The SCALES OKN will potentiate the development of new AI solutions that will benefit the judiciary, legal scholars, and the public. In this article, we outline some of key financial, technical, and policy challenges to developing novel AI solutions.

Knowledge graphs: Introduction, history, and perspectives

“…2022) and (Pah et al. 2022) in this special issue. The resulting KG could be used to track the Acma supply chain and help identify stressed suppliers whose risk may be worth monitoring.…”

“…The court records OKN project described in this special issue makes an extensive use of similar entity extraction techniques (Pah et al. 2022).…”

“…A company could create a "360degree view" of its customers by combining third-party data, for example, Factset and information from the open financial news with the company's own internal databases. This often requires solving the entity disambiguation problem to uniquely identify entities under question-which is also a problem being addressed in the OKN-related projects described in (Cafarella et al 2022) and (Pah et al 2022) in this special issue. The resulting KG could be used to track the Acma supply chain and help identify stressed suppliers whose risk may be worth monitoring.…”