Prediction-Early Warning-Prevention: The Value Orientation, Future Dimension, and Practical Approach of Artificial Intelligence-Enabled Safety Management

Abstract

Against the backdrop of digital transformation and increasingly complex risks, artificial intelligence enables safety management to shift from passive response to active prevention. Artificial intelligence employs technologies such as machine learning, edge computing, and digital twins to enhance the accuracy of risk prediction and the sensitivity of early warning response, thereby constructing a closed loop of the prevention system. In terms of technological application, challenges such as algorithmic bias and human-machine trust crises emerge. Therefore, this study proposes a "three-dimensional collaboration" framework that includes formulating ethical guidelines for artificial intelligence, ensuring human control over key decisions, and promoting public safety literacy education. In the future, attention should be directed toward technological integration, scenario expansion, and governance innovation, driving the intelligent and systematic upgrading of safety management.