Towards Artificial Intelligence Governance in Public Administration

Main Article Content

Juan Manuel Gómez Rodriguez

Abstract

This paper examines the implementation of AI in Public Administration from the perspective of its governance. To this end, it explores the advan- tages and challenges posed by the applications of AI systems in the gover- nmental field, through the use of a deductive and analytical methodology, which seeks to put forward proposals for their effective regulation. The use of AI systems increases government capacity in forecasting public ser- vices, improves their quality and accuracy, citizen reliability, as well as contributes to the promotion of governance, by strengthening values such as efficiency, control and political neutrality. However, in the face of the challenges involved in transparency and clarity in their configuration, in order to know how AI systems work in the government sphere, it is necessary to establish institutional structures that coordinate efforts for their ap- propriateness in order to achieve good governance, as a balance between decisions efficient and objective and the satisfaction of the needs, demands and interests of the citizens. For this purpose, it is essential to instruct ins- titutional collaboration and organize citizen participation, which allow to continuously evaluate its functioning, correct shortcomings and make the necessary adjustments to guarantee the protection of people’s rights.

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Author Biography

Juan Manuel Gómez Rodriguez, Universidad Autónoma del Estado de Morelos

Profesor investigador de tiempo completo. Docente en los programas de Licenciatura en Derecho, Maestría en Derecho y el Doctorado en Derecho y Globalización de la Facultad de Derecho y Ciencias Sociales de la UAEM. Miembro del Sistema Nacional de Investigadores Nivel 1 de la SECIHTI, con perfil deseable PRODEP de la SEP.

How to Cite

Gómez Rodriguez, Juan Manuel. 2026. “Towards Artificial Intelligence Governance in Public Administration”. Foro: Law Journal, no. 45 (January): 29-47. https://doi.org/10.32719/26312484.2026.45.2.

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