Neuro symbolic AI for assessing employee mental health

Abstract

In the rapidly evolving corporate landscape, employee mental well-being has become integral to productivity and organizational success. This thesis introduces a groundbreaking Neuro-Symbolic Artificial Intelligence (NSAI) framework that integrates conversational data analysis to monitor and enhance workplace mental health. At its core, the Mentalisys Health Application leverages H2OWave to provide user-friendly dashboards equipped with real-time sentiment analysis, stress, and depression detection capabilities. A novel Commonsense-Driven Symbolic ReAct-NLI (CSR-NLI) technique, based on OpenAI’s language models, combines symbolic reasoning and natural language inference to uncover causality in workplace communication. Through interactive admin and user-specific dashboards, the system fosters proactive mental health interventions and personalized support, promoting a healthier workplace environment. The study’s primary contribution lies in advancing NSAI for robust causal understanding, going beyond conventional sentiment analysis. Results demonstrate significant potential in improving employee well-being and productivity via timely interventions and precise health risk assessments. This work underscores the transformative role of AI in addressing real-world mental health challenges, driving organizational growth, and enhancing employee satisfaction, while setting a new benchmark for AIdriven solutions in corporate mental health management.