Large Language Models and Artificial Intelligence for Police Report Writing

Abstract

Large Language Models (LLMs), such as ChatGPT, are advanced artificial intelligence systems capable of understanding and generating human-like text. They are trained on vast amounts of textual data, enabling them to comprehend context, answer questions, generate summaries, and even engage in meaningful conversations. As these models continue to evolve, their potential applications in various industries, including law enforcement, are becoming more apparent, as are the potential threats https://www.europol.europa.eu/publications-events/publications/chatgpt-impact-of-large-language-models-law-enforcement . One particularly promising area of application for LLMs in policing is report writing. As many police executives know, not all officers possess strong writing skills, which can lead to inaccurate or incomplete reports. This can have serious consequences for criminal prosecutions, as well as expose departments to civil liability concerns. Implementing LLMs like ChatGPT for report-writing assistance may help address these issues. Even if not fully implemented at the agency level, officers across the country are already using these tools to help in their report generation. Given the stakes, it is wise for agencies to have a sophisticated view and policy on these tools. This paper introduces practitioners to LLMs for report writing, considers the implications of using such tools, and suggests a template-based approach to deploying the technology to patrol officers.

Publication
CrimRxiv

Summary

Police officers are already using AI writing tools like ChatGPT to help write reports, but many departments lack clear policies about this practice. Poor report writing by officers can undermine criminal cases and expose police departments to lawsuits, so AI assistance could potentially improve report quality and reduce these risks. The research suggests that police departments need to develop guidelines for how officers can safely and effectively use these AI tools rather than ignoring their widespread informal use.

(AI-generated summary, v1, January 2026)

Citation Information

Citations: 6 (as of January 2026)

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