The Theory of AI-Powered Legal Transformation (AILT): A New Paradigm in Judicial Systems

Abstract

This study investigates the transformational potential of artificial intelligence (AI) in legal systems by developing and empirically testing the AI-Powered Legal Transformation (AILT) theory. The study looks into how AI technologies, such as natural language processing (NLP), machine learning (ML), and AI-powered decision support systems, can improve operational efficiency, judicial correctness, and ethical safeguards in legal processes. The findings confirm the theory's significant constructs: AI Capabilities, Operational Efficiency, Judicial Accuracy, Ethical Safeguards, Bias Mitigation, and human collaboration, using a qualitative study design that included semi-structured interviews, case studies, and document analysis. The findings reveal that AI dramatically increases efficiency by automating mundane operations and improving the accuracy of legal decisions through data-driven insights. However, the study underlines the significance of ethical safeguards and human monitoring in preventing biases and ensuring transparency in AI-driven judicial systems. While the study provides valuable information, it needs to be improved by its small sample size and emphasis on mature legal systems. Future studies should broaden its scope to cover other jurisdictions and investigate AI's evolving position in legal education and policy. This research adds to the expanding knowledge of AI's integration into law by proposing a theoretical framework for its responsible adoption.