Risk Assessment

Risk assessment, aiming to quantify and manage potential hazards across diverse domains, is undergoing a transformation driven by advancements in artificial intelligence. Current research emphasizes the development of automated risk assessment systems using machine learning models like convolutional neural networks, long short-term memory networks, transformers, and Bayesian networks, often incorporating data from multiple sources (e.g., text, images, sensor data) for improved accuracy and real-time capabilities. These improvements have significant implications for various sectors, including healthcare (suicide detection, disease risk prediction), autonomous systems (driving safety, robot reliability), and infrastructure management (predictive maintenance, disaster risk reduction), enabling more proactive and data-driven decision-making. Furthermore, research is actively addressing challenges related to bias mitigation, uncertainty quantification, and the responsible use of AI in risk assessment.