Academic Journal
Automatic learning model to predict transparency indicators for effective management of public resources ; Modelo de aprendizaje automático para pronóstico de indicadores de transparencia para la gestión efectiva de los recursos públicos
| العنوان: | Automatic learning model to predict transparency indicators for effective management of public resources ; Modelo de aprendizaje automático para pronóstico de indicadores de transparencia para la gestión efectiva de los recursos públicos |
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| المؤلفون: | Ramírez Pérez, Natalia Andrea, Gómez Vargas, Ernesto, Vacca González, Harold |
| المصدر: | Ingeniería Solidaria; Vol. 19 No. 3 (2023); 1-21 ; Ingeniería Solidaria; Vol. 19 Núm. 3 (2023); 1-21 ; Ingeniería Solidaria; v. 19 n. 3 (2023); 1-21 ; 2357-6014 ; 1900-3102 |
| بيانات النشر: | Universidad Cooperativa de Colombia |
| سنة النشر: | 2023 |
| المجموعة: | Revistas de la Universidad Cooperativa de Colombia |
| مصطلحات موضوعية: | corruption, artificial intelligence, machine learning, risk, transparency, corrupción, inteligencia artificial, aprendizaje automático, riesgo, transparencia |
| الوصف: | Introduction: This article is the product of the application of predictive analytical models to measures and indicators of corruption risk, as researched by the Pascual Bravo University Institution and the Francisco José de Caldas District University in 2022 for doctoral research on the risk model for state transparency. Problem: From measurements of institutional capacities, it is possible to generate anticorruption measurements, such is the case of the National AntiCorruption Index (INAC for its Spanish acronym). However, there are improvements to be made in the indicators and the need to incorporate more and better measurements that support this scourge that has long been manifested in Colombia. Objective: The objective of this research is to emphasize the need to take advantage of open data, to generate measurements of state institutional corruption and, therefore, metrics that support its transparency and integrity based on predictive analytical models to generate predictions about government indices. Methodology: First, the importance of generating measurements for the management of corruption cases is pointed out. Then, the application of predictive analytical models to predict scores of the National AntiCorruption Index is evidenced, finding the best model to finally make a forecast based on the identification of the relevant variables. Results: The implementation of higher levels of digital government (egovernment) can significantly contribute to the fight against corruption and the generation of better public policies that support controls and sanctions. It not only facilitates citizen access to state services, but also allows for more open and agile access to data. This constantly promotes transparency at all levels and at all times. The Huber regression that has been implemented, its smaller penalty function, and its linear rather than quadratic growth, make it more suitable for dealing with outliers. This improves the error meter estimates and provides a good estimate of the National AntiCorruption ... |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | Spanish; Castilian |
| Relation: | https://revistas.ucc.edu.co/index.php/in/article/view/4784/3628; https://revistas.ucc.edu.co/index.php/in/article/view/4784 |
| DOI: | 10.16925/2357-6014.2023.03.09 |
| الاتاحة: | https://revistas.ucc.edu.co/index.php/in/article/view/4784 https://doi.org/10.16925/2357-6014.2023.03.09 |
| Rights: | Derechos de autor 2023 Ingeniería Solidaria ; https://creativecommons.org/licenses/by/4.0 |
| رقم الانضمام: | edsbas.37F17D4F |
| قاعدة البيانات: | BASE |
| DOI: | 10.16925/2357-6014.2023.03.09 |
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