Paper Contents
Abstract
Safety has always been central to human progress, but in many industries, the traditional approach to safety has been reactiveintervening only after an accident or hazard has already occurred. With the rise of artificial intelligence (AI) and machine learning (ML), a new paradigm is emerging: predictive safety analytics. This involves using historical and real-time data to forecast risks before they escalate into incidents. By applying algorithms capable of recognizing hidden patterns and anomalies in data streams, organizations can now anticipate equipment failures, unsafe human behavior, or hazardous conditions long before they cause harm. This paper provides a conceptual exploration of how AI and ML are transforming safety analytics. It explains the foundations of predictive safety, discusses applications across industries, highlights the benefits and challenges, and projects future directions where predictive safety analytics will play a critical role in building resilient, ethical, and human-centered workplaces and societies.
Copyright
Copyright © 2025 Lavanya M Research Paper- Lavanya M. This is an open access article distributed under the Creative Commons Attribution License.
