Integrating Artificial Intelligence in Risk Assessment to Enhance Workplace Safety Protocols
Abstract
The integration of Artificial Intelligence (AI) into risk assessment processes is increasingly recognized as a transformative approach to improving workplace safety across various industries. Traditional safety protocols often rely on reactive strategies and manual evaluations that are prone to human error and inefficiency. This study investigates the implementation of AI technologies—specifically machine learning and predictive analytics—to proactively identify, assess, and mitigate occupational hazards. The objective is to enhance the accuracy and timeliness of risk detection, enabling real-time decision-making and dynamic safety management. The research adopts a qualitative-descriptive method complemented by a case study approach in industrial environments with high safety demands. Data were gathered through expert interviews, system evaluations, and AI-based simulation tools. Findings indicate that AI-driven risk assessment systems significantly reduce incident rates by identifying patterns in historical data and predicting potential failures before they occur. Furthermore, the integration of AI enables continuous monitoring and adaptive protocol updates, fostering a culture of preventative safety. The synthesis of results underscores the potential of AI not only to optimize current safety frameworks but also to set new standards for proactive workplace risk management. In conclusion, embedding AI into risk assessment processes represents a strategic advancement in occupational safety, providing a more resilient and responsive framework for hazard prevention.
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