Northwestern University researchers are using artificial intelligence (AI) to help anyone who needs it gain meaningful access to the information in federal court records, without the need for data and analytic skills. Researcher Kristen Hammond and the C3 Lab are developing an AI platform that provides users with access to the information and insights hidden inside federal court records, regardless of their data and analytic skills or their financial resources.

Even though this data is publicly available, in a practical sense it is not accessible to many who need it. "The problem with court data is the same problem with a lot of datasets," said Hammond. "The data cost money, and the technical skills to use them cost money. That means very few people have access — not just to the data — but the information that we all need that's hidden inside of it."

Without fair access to this information, which includes the ability to search the database in a productive and user-friendly way as well as easily connect relevant information from the database to other public data, the average person is at a huge disadvantage. The tool developed by the team allows users to answer questions such as: How do different judges affect the outcomes of similar cases? Does it make a difference to be defended by a big law firm compared to a smaller one? How many cases settle? Ultimately, the goal is to ensure that the court system acts fairly.

The team, consisting of computer and data scientists, legal scholars, journalists and policy experts, looked at judicial waiver decisions. Citizens can file an application to waive a $400 filing fee when filing a lawsuit. The team found that the decisions of judges varied greatly because there is no uniform standard for review of these requests. In one district, the number of approved waivers ranged from less than 20% to more than 80% depending on the judge.

The researchers believe that giving the public the ability to access and analyze court records is the best way to address the problem. Their recommended approach includes eliminating the paywall as a barrier to the information; linking the courtroom data to external data on judges, litigants and lawyers; and providing access to information gained from analysis of federal court data.

Toward that end, they are developing SCALES-OKN (Systematic Content Analysis of Litigation Events Open Knowledge Network), an AI-powered platform that makes the federal courtroom data and insights available to the public.

The insights are published in the journal Science.