Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach
Param Kulkarni, Yingchi Liu, Hao-Ming Fu, Shaohua Yang, Isuru Gunasekara, Matt Peloquin, Noah Spitzer-Williams, Xiaotian Zhou, Xiaozhong Liu, Zhengping Ji, Yasser Ibrahim
TL;DR
The paper addresses the challenge of producing accurate police reports from noisy ASR transcripts while protecting the rights of suspects, officers, and the public. It proposes a trust-centered pipeline that integrates body-worn camera ASR with a safety-aware LLM and a structured human-in-the-loop workflow to generate draft reports without making predictive claims about incidents. Key contributions include the INSERT-based refinement mechanism, mandatory officer signing, and an evaluation showing improved terminology and coherence, along with substantial time savings. The work demonstrates practical impact through pilot deployment across hundreds of agencies, highlighting potential gains in consistency, accountability, and efficiency in policing practice.
Abstract
Achieving a delicate balance between fostering trust in law enforcement and protecting the rights of both officers and civilians continues to emerge as a pressing research and product challenge in the world today. In the pursuit of fairness and transparency, this study presents an innovative AI-driven system designed to generate police report drafts from complex, noisy, and multi-role dialogue data. Our approach intelligently extracts key elements of law enforcement interactions and includes them in the draft, producing structured narratives that are not only high in quality but also reinforce accountability and procedural clarity. This framework holds the potential to transform the reporting process, ensuring greater oversight, consistency, and fairness in future policing practices. A demonstration video of our system can be accessed at https://drive.google.com/file/d/1kBrsGGR8e3B5xPSblrchRGj-Y-kpCHNO/view?usp=sharing