The NSF Convergence Accelerator supports use-inspired, team-based, multidisciplinary efforts that address challenges of national importance and will produce deliverables of value to society in the near future.
This project will develop the Systematic Content Analysis of Legal EventS Open Knowledge Network (SCALES OKN). The SCALES OKN seeks to create the computational and data science tools needed to democratize access to court records. Greater access to court records and analysis tools will enable policy makers, scholars, journalists, entrepreneurs, and the public to directly engage with and evaluate the workings of the U.S. courts.
The U.S. court system collects detailed data about their activities, but the challenge is that most of this data sits behind paywalls and in scattered systems that are difficult to access. Highly limited access means that court records are functionally inaccessible to the public. This limited access to court records has prevented the development of tools to turn court data into information and insights. The SCALES OKN will develop aggregation and analysis tools that will bring together a community of public servants, academic institutions, non-profits, private organizations, and individuals to better understanding how litigation proceeds. Access to these new data and analysis tools will enable legal scholars to better analyze litigation processes, entrepreneurs to assess litigation costs and risk, journalists to investigate equity in outcomes, advocacy organizations assess public policy needs, and the public to better understand how the modern judiciary functions.
This project joins 22 scholars in computer and data science, economics, journalism, law, and sociology from eight universities with a large and diverse range of partners from non-profit and for-profit organizations. The SCALES OKN’s existing partnerships will enable users to ask questions such as how lawsuits involving Fortune 500 companies or with representation from large law firms progress, or if judicial rules are consistently implemented. As the project develops, additional data and tools will enable an even richer view into topics such as how new laws impact the judiciary, corporations, and individuals, or how a changing economic climate impacts people and organizations—whether that be because of a global economic downturn or changes to the nature of employment as impacts from the COVID-19 epidemic unfold.
This team is building SCALES OKN as an open and freely accessible knowledge network. Their efforts include developing the tools to transform the data that define court records into actionable information. This work will include the development of tools to extract and transform data from court records, resolve and disambiguate entities, and enable the automated identification of litigation events and construction of a lawsuit’s lifecycle. Rather than having users depend on their own data skills, the SCALES efforts plan to map user information requests onto the analyses needed to address questions of relationships, correlations, trends, and distributions of actions and decisions in the legal system. The team also plans to build tools that facilitate the continued growth of open knowledge networks through public contributions. The project will leverage machine learning to enable users to develop further ontologies and merge additional datasets to answer novel questions. Importantly, these advances will allow for the rapid expansion of natural language processing techniques to legal contexts and catalyze further computational analysis of the law.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
