Artificial Intelligence and Deficit of Personnel in the Romanian Police

Abstract

The purpose of the article. Staff shortages in Romania’s Police and Border Police have reached critical levels, with vacancy rates of 15% to over 28% in several counties. These shortfalls impose direct financial burdens through overtime, standby pay, and productivity losses while also degrading service quality. This article assesses whether — and to what extent artificial intelligence can offset these gaps and reduce the related fiscal strain. Drawing on international case studies, Romanian staffing data, and recent literature on public-sector digitalization, the study argues that targeted AI deployment can shift the emphasis from a quantitative staffing model to a qualitative efficiency model. The central hypothesis is that automating administrative and surveillance tasks with AI could substitute for roughly 15–25% of current personnel vacancies in Romanian law enforcement without proportional budget increases. The article also addresses principal risks algorithmic bias, privacy concerns, and governance gaps and proposes a phased implementation framework consistent with the EU regulatory requirements.

Methodology: The study uses comparative cases, Romanian staffing data, and EU – national policy analysis to assess AI’s governance and fiscal implications in policing, acknowledging its conceptual, document‑based design and lack of primary data.

Results of the research: Romania’s severe police vacancies create safety and fiscal pressures, with understaffing driving overtime, reduced service quality, and declining trust. Targeted AI could offset part of the gap cost-effectively, but cannot replace human roles and requires strong governance. A phased national strategy and further empirical research remain essential for future implementation.