Uncovering Hidden Realities of Child Labor Abuse in Egyptian Workplaces with Machine Learning and Explainable AI

Child labor abuse is a pervasive global problem, especially in Egyptian workplaces, where its bureaucratic processes make it difficult to detect and intervene such phenomena.This research presents an in-depth investigation combining explainable artificial intelligence (XAI) and machine learning (ML) to uncover the hidden reality of child labor exploitation in Egyptian companies.Applying six different machine learning models and analyzing, on a published dataset of mistreatment of employed children in Egypt, Artificial Neural Network (ANN) model turns out to performs better than the other models.It shows improvements in accuracy, precision, recall, F1-score, and AUC, over other models including Random Forest, Support Vector Classifier (SVC), and AdaBoost, with an accuracy of 96.00%.Additionally, Explainable AI methods like SHAPASH analysis offer insightful information about the variables, such as working sector and night shift work, driving abuse predictions.With its data-driven method to reveal hidden realities, this research not only promotes a safer environment for kids in Egyptian workplaces, but also these study findings can be expanded in identifying other form of abuses such as elder abuse, substance abuse and others.