Integration of artificial intelligence-based solutions into electronic gaming machines for responsible gambling: a...

The increase in sophisticated electronic gambling products offered by gambling manufacturers facilitates an increase in the accessibility of gambling platforms and modes. This study proposes utilising Artificial Intelligence (AI) to enhance self-awareness and self-control within electronic gaming machines. The study adopts a case study that follows an exploratory research design, as the causal explanation argument suggests that the Electronic Gaming Machine (EGM) device causes problem gambling. Secondary data on gambling were obtained and analysed. The machine-learning pattern recognition and classification model; a learning approach which learns from a trained dataset to make decisions or predictions, was employed. The dataset was trained iteratively using the scaled conjugate gradient backpropagation with the input and output target samples divided into training, validation, and test datasets. Furthermore, the softmax was used for classifying the dataset into three classes: responsible, intermediate, and irresponsible gambling. The confusion matrix was used to analyse the percentages of correct and incorrect classifications. The results obtained indicated that the accuracy of the developed model was 99.20%, while the precision was 85.70%. The recall achieved 85.70%, while the F1-score reached 80.50%. The closeness of these performance indices to 1, coupled with the negligible value of mean square error, indicates that the developed classification model is robust and suitable for classification problems. Thus, this study contributes to knowledge by developing an AI model that can track players and reduce harm in a land-based gambling environment.