IA aplicada a la identificación de características de ocupación de menores en hogares colombianos para detectar posible trabajo infantil - Romero-Luna, Roxana-María; Ordoñez-Erazo, Hugo-Armando; Cobos-Lozada, Carlos-Alberto (Revista Científica, 2024)
Child labor is an age-old global issue that tends to be normalized in certain cultural, social, and economic contexts. Colombia, a developing country affected by armed conflict and its challenging topography, also faces a lack of employment opportunities for mothers or heads of households. These factors, along with the challenges in accessing education, make the country vulnerable to child labor. This article uses data from the 2022 Quality of Life Survey of the National Administrative Department of Statistics (DANE), specifically from the child labor module. With these data, an artificial intelligence (AI) approach based on machine learning algorithms is proposed to classify the occupation of minors in Colombian households. It was found that the optimal model is obtained by applying the DecisionTreeClassifier algorithm to duly processed data and using stratified cross-validation. By analyzing the selected attributes, which are the most important and highest ranked, it is observed that the occupation of minors is strongly influenced by household economic characteristics and composition. It is concluded that AI techniques are crucial for identifying the factors that influence social problems such as child labor, and that they can serve as support for government entities in implementing mitigation and prevention strategies.