Designing AI for Prosecutorial Governance: Case Prioritization and Statutory Oversight in Mexico

TL;DR

This paper develops and evaluates a machine learning system to assist Mexican prosecutors in case prioritization, aiming to reduce backlog and improve efficiency through data-driven decision support.

Contribution

It introduces a novel ML-based case triage system co-developed with prosecutors, focusing on interpretability and operational integration within existing workflows.

Findings

Random Forest classifiers achieve 0.74 Precision@300.

System supports prosecutors in identifying actionable cases efficiently.

Potential to improve prosecutorial throughput without workflow disruption.

Abstract

Prosecutors across Mexico face growing backlogs due to high caseloads and limited institutional capacity. This paper presents a machine learning (ML) system co-developed with the Zacatecas State Prosecutor's Office to support internal case triage. Focusing on the M\'odulo de Atenci\'on Temprana (MAT) -- the unit responsible for intake and early-stage case resolution -- we train classification models on administrative data from the state's digital case management system (PIE) to predict which open cases are likely to finalize within six months. The model generates weekly ranked lists of 300 cases to assist prosecutors in identifying actionable files. Using historical data from 2014 to 2024, we evaluate model performance under real-time constraints, finding that Random Forest classifiers achieve a mean Precision@300 of 0.74. The system emphasizes interpretability and operational feasibility, and we will test it via a randomized controlled trial. Our results suggest that data-driven prioritization can serve as a low-overhead tool for improving prosecutorial efficiency without disrupting existing workflows.