Abstract
The increased prevalence of mental health issues in the workplace affects employees’ wellbeing
and organisational success, necessitating proactive interventions such as employee
assistance programmes, stress management workshops, and tailored wellness initiatives.
Artificial intelligence (AI) techniques are transforming mental health risk prediction using
behavioural, environmental, and workplace data. However, the “black-box” nature of
many AI models hinders trust, transparency, and adoption in sensitive domains such as
mental health. This study used the Open Sourcing Mental Illness (OSMI) secondary dataset
(2016–2023) and applied four ML classifiers, Random Forest (RF), xGBoost, Support Vector
Machine (SVM), and AdaBoost, to predict workplace mental health outcomes. Explainable
AI (XAI) techniques, SHapley Additive exPlanations (SHAP) and Local Interpretable
Model-agnostic Explanations (LIME), were integrated to provide both global (SHAP) and
instance-level (LIME) interpretability. The Synthetic Minority Oversampling Technique
(SMOTE) was applied to address class imbalance. The results show that xGBoost and RF
achieved the highest cross-validation accuracy (94%), with xGBoost performing best overall
(accuracy = 91%, ROC AUC = 90%), followed by RF (accuracy = 91%). SHAP revealed
that sought_treatment, past_mh_disorder, and current_mh_disorder had the most significant
positive impact on predictions, while LIME provided case-level explanations to support
individualised interpretation. These findings show the importance of explainable ML
models in informing timely, targeted interventions, such as improving access to mental
health resources, promoting stigma-free workplaces, and supporting treatment-seeking
behaviour, while ensuring the ethical and transparent integration of AI into workplace
mental health management.