Sesgo de datos en aplicaciones de aprendizaje automático: un estudio de caso de un modelo no supervisado para identificar el riesgo de corrupción en la contratación pública colombiana

This study analyzes data bias in an unsupervised learning algorithm designed to identify the risk of corruption in public procurement of Colombia. The employed algorithm is a two-stage clustering model used to segment electronic contracts based on variables indicating corruption risk. The objective was to develop an early warning tool for corruption in the Programa de Alimentación Escolar (PAE) procurement, utilizing data from the Sistema Electrónico para la Contratación Pública (SECOP). Although the results demonstrate the potential of artificial intelligence algorithms for detecting corruption risks, they also reveal significant limitations in their practical implementation, attributable to data availability and quality deficiencies. Specifically, biases of representation, measurement, and omitted variables were identified, affecting the algorithm's reliability. The study provides a detailed analysis of these biases, assessing their impact on the algorithm's performance, and emphasizes the importance of recognizing and addressing biases during the development of such initiatives. Finally, recommendations are presented to improve the quality of data in SECOP, aiming to enhance the reliability and accuracy of these algorithms in future developments.