SCC-IRG track 2: supporting transparency and equity in the criminal legal system through a community-driven digital platform

This National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) Federal Grant Program (CFDA 47.070) award totaling $1,299,743 provides funding to Tulane University from October 1, 2024 to September 30, 2027. The project aims to enhance transparency and accountability within the criminal legal system in New Orleans by developing a comprehensive, community-driven data dashboard and analysis toolkit. The university will partner with Court Watch NOLA and experts in computer science, law, and criminology to integrate fragmented court data, volunteer observations, and public records into a robust data ingestion solution. The project will also develop a data analysis toolkit to isolate determinants of judicial outcomes and implement active knowledge acquisition methods to optimize volunteer efforts. This interdisciplinary approach seeks to empower local communities to monitor and understand the criminal legal system more effectively.

Abstract

This project aims to enhance both transparency and accountability within the criminal legal system in new orleans by developing a comprehensive, community-driven data dashboard and analysis toolkit. The project addresses the critical need for better data infrastructure in a city with high incarceration and exoneration rates, aiming to provide community groups with real-time access to detailed court data. This information will enable informed policy discussions to foster positive changes in the legal system. By partnering with court watch nola and experts in computer science, law, and criminology, the project seeks to empower local communities to monitor and understand the criminal legal system more effectively. The project's goal is to transform the community?s ability to access and analyze court data in real-time. This step in the research will be accomplished through four key initiatives: engaging stakeholders throughout the development process to ensure the system meets diverse needs; creating a robust data ingestion solution that integrates fragmented court data, volunteer observations, and public records using natural language processing and missing data models; developing a data analysis toolkit leveraging causal representation learning to isolate determinants of judicial outcomes, and implementing active knowledge acquisition methods to optimize volunteer efforts. This interdisciplinary approach will advance community-driven data science and contribute to a deeper understanding of equity and bias in the criminal legal system. 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.- subawards are planned for this award.