Computational modeling of logical inference in legal sciences refers to the application of computational methods and algorithms for the analysis, interpretation, and extraction of logical conclusions from legal data and texts. This approach is grounded in the principles of formal logic and rules of inference, employing the computer as a powerful instrument for executing analytical and reasoning processes. In legal systems, the complexity and volume of legal data including statutes, regulations, and judicial precedents underscore the necessity of utilizing such models.The use of computational models in legal reasoning enables the systematic processing and logical evaluation of legal norms, facilitating the extraction of legal relations and key concepts from legal texts. Judges, lawyers, and legal scholars can benefit from these tools to assess the applicability of laws to specific cases, identify relevant precedents, and predict potential outcomes based on available evidence.
Automating the process of legal reasoning not only accelerates legal research and judicial analysis but also enhances the coherence of legal arguments and reduces the likelihood of human error. Moreover, computational models are capable of detecting potential contradictions and inconsistencies within legal texts, thereby contributing to the development and refinement of more coherent and efficient legal frameworks.Thus, computational modeling of legal inference can be regarded as an innovative instrument in the service of justice—one that, by leveraging artificial intelligence technologies, significantly strengthens the capacity of legal systems to analyze and interpret laws with greater precision and transparency.
Keywords: inference and legal inference, artificial intelligence, inference of laws, interpretation of law, text mining.