Inteligencia artificial: impactos y desafíos en las contrataciones públicas. Revisión sistemática
Bibliographic record
Abstract
La inteligencia artificial (IA) ha generado diversos impactos y desafíos en los procesos de las contrataciones públicas, siendo fundamentales su aplicación para el desarrollo económico e inclusión social. Se justifica por la persistencia de la corrupción, barreras regulatorias y falta de sostenibilidad del estado. El objetivo consistió en analizar factores que afectan la eficiencia, transparencia, inclusión, sostenibilidad, innovación tecnológica, normativas y el desarrollo económico interno. La metodología aplicó una revisión sistemática basada en artículos indexados en Scopus, utilizando filtros temáticos, geográficos y de idioma. alcanzando 50 estudios relevantes de países como Brasil, Estados Unidos, Canadá, Perú, México y otros. Los resultados revelaron que la IA identifico la centralización de competencias limita la transparencia y la eficiencia del gasto público. La corrupción fue un problema estructural en América Latina, mientras que, en EE.UU., demostró transparencia y sostenibilidad de costos logrando iniciativas exitosas. La IA, como parte de la innovación tecnológica mejoró la eficiencia, aunque enfrentó desafíos de implementación logrando obtener la conclusión en fundamentos para el desarrollo, reduciendo obstáculos regulatorios que limitaron su efectividad en la gestión pública.//Artificial Intelligence (AI) has generated diverse impacts and challenges in public procurement processes, with its application being fundamental for economic development and social inclusion. This is justified by the persistence of corruption, regulatory barriers, and the lack of state sustainability. The objective was to analyze factors that affect efficiency, transparency, inclusion, sustainability, technological innovation, regulations, and internal economic development. The methodology applied a systematic review based on articles indexed in Scopus, using thematic, geographic, and language filters, reaching 50 relevant studies from countries such as Brazil, the United States, Canada, Peru, Mexico, and others. The results revealed that AI identified the centralization of powers that limits the transparency and efficiency of public spending. Corruption was a structural problem in Latin America, while in the US, it demonstrated transparency and cost sustainability, achieving successful initiatives. AI, as part of technological innovation, improved efficiency, although it faced implementation challenges, managing to reach the conclusion, on the grounds for development, reducing regulatory obstacles that limited its effectiveness in public management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.021 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".