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

Fernanda Sobrino (), Adolfo De Unánue (), Edgar Hernández (), Patricia Villa, Elena Villalobos (), David Aké (), Stephany Cisneros (), Cristian Paul Camacho Osnay (), Armando García Neri () and Israel Hernández ()
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Fernanda Sobrino: School of Government and Public Transformation, Tecnológico de Monterrey
Adolfo De Unánue: School of Government and Public Transformation, Tecnológico de Monterrey
Edgar Hernández: School of Government and Public Transformation, Tecnológico de Monterrey
Patricia Villa: School of Government and Public Transformation, Tecnológico de Monterrey
Elena Villalobos: School of Government and Public Transformation, Tecnológico de Monterrey
David Aké: School of Government and Public Transformation, Tecnológico de Monterrey
Stephany Cisneros: School of Government and Public Transformation, Tecnológico de Monterrey
Cristian Paul Camacho Osnay: Office of the Attorney General of the State of Zacatecas, Mexico
Armando García Neri: Office of the Attorney General of the State of Zacatecas, Mexico
Israel Hernández: Office of the Attorney General of the State of Zacatecas, Mexico

No 24, Working Paper Series of the School of Government and Public Transformation from School of Government and Public Transformation, Tecnológico de Monterrey

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 Módulo de Atención 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.

Keywords: Artificial intelligence; Digital government; Criminal justice; Algorithmic governance; Case prioritization; Public sector AI; Decision support systems; Mexico (search for similar items in EconPapers)
JEL-codes: C38 H83 K42 O33 (search for similar items in EconPapers)
Pages: 75 pages
Date: 2026-02
New Economics Papers: this item is included in nep-law
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https://egobiernoytp.tec.mx/sites/default/files/20 ... itization_mexico.pdf First version, 2026 (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:gnt:wpaper:24

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