Bayesian deep learning: An enhanced AI framework for legal reasoning alignment

The integration of artificial intelligence into the field of law has penetrated the underlying logic of legal operations. Currently, legal AI systems face difficulties in representing legal knowledge, exhibit insufficient legal reasoning capabilities, have poor explainability, and are inefficient in handling causal inference and uncertainty. In legal practice, various legal reasoning methods (deductive reasoning, inductive reasoning, abductive reasoning, etc.) are often intertwined and used comprehensively. However, the reasoning modes employed by current legal AI systems are inadequate. Identifying AI models that are more suitable for legal reasoning is crucial for advancing the development of legal AI systems.

Distinguished from the current high-profile large language models, we believe that Bayesian reasoning is highly compatible with legal reasoning, as it can perferm abductive reasoning, excel at causal inference, and admits the "defeasibility" of reasoning conclusions, which is consistent with the cognitive development pattern of legal professionals from apriori to posteriori. AI models based on Bayesian methods can also become the main technological support for legal AI systems. Bayesian neural networks have advantages in uncertainty modeling, avoiding overfitting, and explainability. Legal AI systems based on Bayesian deep learning frameworks can combine the advantages of deep learning and probabilistic graphical models, facilitating the exchange and supplementation of information between perception tasks and reasoning tasks. In this paper, we take perpetrator prediction systems and legal judegment prediction systems as examples to discuss the construction and basic operation modes of the Bayesian deep learning framework. Bayesian deep learning can enhance reasoning ability, improve the explainability of models, and make the reasoning process more transparent and visualizable. Furthermore, Bayesian deep learning framework is well-suited for human-machine collaborative tasks, enabling the complementary strengths of humans and machines.