Automatic identification and processing model of artificial intelligence service tort liability based on big data analysis

This article focuses on the research of automatic identification and processing model of artificial intelligence (AI) service tort liability based on big data analysis. With the complexity of infringement cases increasing, the efficiency and accuracy of traditional manual processing methods are limited, so the aim is to build an efficient and accurate automatic processing model of tort liability. By integrating the three sub-models of tort liability determination, compensation calculation and liability assumption, the performance of the model is comprehensively analyzed by using big data analysis technology and adopting various feature selection strategies and model evaluation indicators. The results show that the accuracy rate of the model is over 0.9 in some types such as personal injury infringement and property damage infringement. The overall accuracy of the comprehensive model is 0.88, the error rate of the average compensation amount is 0.07, and the accuracy rate of judging the mode of responsibility is 0.89. It shows that the model based on big data analysis has good performance and can provide effective support for the actual tort liability handling.