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Federal court records have been available online for nearly a quarter century, yet they remain frustratingly inaccessible to the public. This is due to two primary barriers: (1) the federal government's prohibitively high fees to access the records at scale and (2) the unwieldy state of the records themselves, which are mostly text documents scattered across numerous systems. Official datasets produced by the judiciary, as well as third-party data collection efforts, are incomplete, inaccurate, and similarly inaccessible to the public. The result is a de facto data blackout that leaves an entire branch of the federal government shielded from empirical scrutiny. In this Essay, we introduce the SCALES project: a new data-gathering and data-organizing initiative to right this wrong. SCALES is an online platform that we built to assemble federal court records, systematically organize them and extract key information, and-most importantly-make them freely available to the public. The database currently covers all federal cases initiated in 2016 and 2017, and we intend to expand this coverage to all years. This Essay explains the shortcomings of existing systems (such as the federal government's PACER platform), how we built SCALES to overcome these inadequacies, and how anyone can use SCALES to empirically analyze the operations of the federal courts. We offer a series of exploratory findings to showcase the depth and breadth of the SCALES platform. Our goal is for SCALES to serve as a public resource where practitioners, policymakers, and scholars can conduct empirical legal research and improve the operations of the federal courts. For more information, visit www.scales-okn.org. 

Abstract We implemented a user-centered approach to the design of an artificial intelligence (AI) system that provides users with access to information about the workings of the United States federal court system regardless of their technical background. Presently, most of the records associated with the federal judiciary are provided through a federal system that does not support exploration aimed at discovering systematic patterns about court activities. In addition, many users lack the data analytical skills necessary to conduct their own analyses and convert data into information. We conducted interviews, observations, and surveys to uncover the needs of our users and discuss the development of an intuitive platform informed from these needs that makes it possible for legal scholars, lawyers, and journalists to discover answers to more advanced questions about the federal court system. We report on results from usability testing and discuss design implications for AI and law practitioners and researchers.