Machine Learning for Risk Assessment in Employee Safety Compliance

Abstract

Machine learning (ML) is revolutionizing employee safety compliance across multiple industries most notably, construction, manufacturing, and healthcare. In this paper, present the use of ML models decision trees, neural networks, and computer vision techniques - for predicting, monitoring, and mitigating workplace hazards. ML provides risk assessment by analyzing archival data, environmental conditions, and real-time inputs from IoT sensors and wearable devices for a continuous safety monitoring. In particular, XAI helps fill transparency and interpretability gaps, providing actionable insights. This study demonstrates the fundamental role that ML plays in decreasing the occurrence of accidents in the workplace and improving workplace safety and compliance while pushing for proactive risk management.