A user-centered approach to developing an AI system analyzing U.S. federal court data

Rachel F. Adler*, Andrew Paley, Andong L. Li Zhao, Harper Pack, Sergio Servantez, Adam R. Pah, Kristian J Hammond, Scales Okn Consortium

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalArtificial Intelligence and Law
DOIs
StateAccepted/In press - 2022

Keywords

  • Data analytics
  • Natural language processing
  • Notebook interface
  • User-centered design
  • Visualization

ASJC Scopus subject areas

  • Law
  • Artificial Intelligence

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