Enhancing Organizational Information Systems Through Explainable Artificial Intelligence

Abstract

This study examines Workplace Perceptions among Finnish employees through the application of Artificial Intelligence within the domain of Human Resource Analytics. An integrated analytical framework combining Clustering Analysis, supervised classification, and Explainable Artificial Intelligence is proposed to uncover and interpret latent employee perception profiles. Using 23 perception-related indicators from the Finnish Working Life Barometer 2022, K-means clustering identified two distinct employee groups: one characterized by consistently positive evaluations of fairness, leadership, well-being, and motivation, and another reflecting systematically negative workplace perceptions. A LightGBM model was subsequently employed to predict cluster membership based on demographic and occupational variables, and SHapley Additive exPlanations (SHAP) were used to provide transparent global and local interpretations of the predictive outcomes. The results show that employment duration, age, industry affiliation, gender, and socioeconomic status are the most influential determinants of cluster membership. By embedding Explainable Artificial Intelligence into Human Resource Analytics, the study demonstrates how employee perception data can be transformed into interpretable knowledge that supports organizational Decision-Support Systems. The proposed framework advances data-driven and transparent HR decision-making and contributes to the United Nations Sustainable Development Goal 8, Decent Work and Economic Growth, by identifying structural disparities in employee experience and enabling more equitable and inclusive workplace interventions.