Risk Model

Risk models are being developed across diverse fields to quantify and predict the likelihood of undesirable events, aiming to improve decision-making and safety. Current research emphasizes the integration of human factors (e.g., driver behavior, user perception) and the application of machine learning techniques, including deep learning, gradient boosting, and neural networks, to create more accurate and nuanced risk assessments. These advancements are impacting various sectors, from autonomous driving and healthcare (e.g., readmission risk prediction) to content moderation and wildfire prediction, by enabling proactive interventions and resource allocation. The focus is shifting towards dynamic risk assessment, incorporating uncertainty, and ensuring model fairness, accountability, and portability across different contexts.