Risks and Mitigation Strategies for Generative Artificial Intelligence in Legal Document Drafting

Abstract

The breakthrough advancements of generative artificial intelligence (GAI) have catalysed a new wave of intelligent technology-driven revolution. Within the legal domain, GAI possesses extensive application potential in legal document drafting due to its robust natural language comprehension capabilities, precise data analysis skills, and highly efficient text generation. Consequently, inherent risks such as data security and algorithmic bias stemming from technical flaws in data and algorithms are increasingly evident. These risks further give rise to derivative concerns, including inadequate content accuracy, weak document explainability, difficulties in responsibility attribution, and deviations in ethical values. However, existing legal frameworks face application challenges in addressing issues surrounding GAI's use in public security legal document drafting, exhibiting supply-side inadequacies in data access, algorithmic review, and accountability mechanisms that struggle to keep pace with technological iteration. Only by coordinating three pathwaystechnological optimisation, institutional refinement, and human-machine collaborative governancecan the challenges of risk regulation in this domain be resolved. This approach unifies data security, algorithmic transparency, and controllable outcomes, thereby promoting the healthy and deep integration of generative AI with public security law enforcement operations.

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