Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach
This research addresses the problem of fairness and transparency in law enforcement reporting for both officers and civilians, which is an incremental step towards improving policing practices.
This study tackled the challenge of generating police report drafts from noisy dialogue data, resulting in high-quality and structured narratives that promote accountability and procedural clarity. The system has the potential to transform the reporting process, but no concrete numbers are provided.
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