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4054. From docket to data: unpacking judicial congestion using process- and queue-mining
Invited abstract in session WC-6: Advancements of OR-analytics in statistics, machine learning and data science 18, stream Advancements of OR-analytics in statistics, machine learning and data science.
Wednesday, 12:30-14:00Room: 1013 (building: 202)
Authors (first author is the speaker)
| 1. | Shany Azaria
|
| Rotman School of Management, University of Toronto |
Abstract
The court system, pivotal for social justice, is overflowed by congestion affecting social welfare, economic development, and access to justice. Despite its significance and operational complexity, being characterized by constrained resources, increasing demand, long in-process waiting time, impatient customers, and LOS measured in years, empirical research on court operations remains limited. This scarcity is largely attributable to challenges in accessing reliable and comprehensive data. This study employs NLP and AI tools to transform two decades of US Federal District Court case dockets into a detailed event log, leveraging process- and queue-mining to explore judicial congestion. By scraping, labeling and analyzing millions of docket entries through operations management lenses, we are able to observe the case flow in the system and assess congestion impacts on case processing. Our findings illuminate the judicial workflow, offering insights for reducing congestion and enhancing system efficiency. This pioneering approach underscores the potential of data-driven analysis in court systems operations.
Keywords
- Analytics and Data Science
- OR/MS and the Public Sector
- Service Operations
Status: accepted
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