Real-Time Justice Intelligence: Ai-Enabled Data Governance Transforming City Criminal Justice

Abstract

Criminal justice systems across U.S. cities continue to struggle with fragmented legacy infrastructure, manual reporting, and inconsistent data practices that undermine both operational efficiency and fairness. This paper presents the Real-Time Justice Intelligence Platform—an AI-enabled, cloud-native data governance architecture deployed in the City of New Orleans to unify historically siloed agency data across police, courts, jail, prosecution, and public defense. The platform integrates Azure Data Factory, Delta Lake, Databricks, and Power BI into an automated pipeline that delivers near-real-time analytics with full auditability, standardized semantic models, and robust privacy controls. This transformation has reduced reporting workload by up to 90%, cut data latency from ~48 hours to under 15 minutes, consolidated more than a dozen siloed reports into a single source of truth, and enabled cross-agency collaboration through shared, governed metrics. Beyond efficiency, the platform enhances fairness by monitoring time-sensitive judicial events (e.g., first appearances, statutory deadlines), preventing unlawful detention, improving caseflow visibility, and strengthening public accountability. Embedded governance frameworks—including row-level security, standardized definitions, and auditability—ensure ethical data use and create a foundation for future AI innovations such as predictive caseload forecasting, anomaly detection, and bias analysis. This paper demonstrates how a modern data Lakehouse, combined with transparent governance, can transform justice operations, advance data equity, and rebuild public trust. The Real-Time Justice Intelligence Platform represents a scalable model for ethically deploying AI-ready infrastructure in public-sector ecosystems.