Public Policy Approaches to Addressing Neurojurisprudential Challenges and Enhancing Legislative Systems

This paper aims to address the potential violation of individual human rights in the use of fMRI data from a technical and legal perspective. First, the core concepts and core challenges of neurojurisprudence are summarized. The application of low-bitwidth neural networks in the simulation of neurojurisprudence data flow is used to improve the quantification effect of network channels through the balance of inter-layer weight ranges. At the same time, layer fusion is introduced to combine information at different levels to strengthen the relevance of fMRI data and legal provisions. In addition, differential privacy technology can better protect personal privacy. The results show that after the model is strengthened, the risk of privacy leakage of fMRI data of 10 volunteers has dropped by up to 16 points, showing the effectiveness of this method in privacy protection during the acquisition of strong fMRI data. The legislative protection strength score has increased to 90, and the evidence effectiveness score has also increased significantly, with the best reaching 88, indicating that the setting of the threshold for evidence adoption in legislation is more reasonable. The legal fairness evaluation has reached or is close to the highest score of 95, indicating that this method focuses on protecting the equal rights and interests of people of different socioeconomic status and effectively reduces possible discriminatory effects. The quantitative method model in this paper provides a favorable reference for the field of neurojurisprudence and legislative system.