Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach

TL;DR

This paper introduces an AI system that automatically drafts police reports from noisy ASR dialogue data, enhancing transparency, accountability, and fairness in law enforcement reporting processes.

Contribution

It presents a novel trust-centered LLM approach that extracts key interaction elements to generate structured police report drafts from complex audio data.

Findings

Successfully extracts key report elements from noisy ASR outputs.

Generates high-quality, structured police report drafts.

Potential to improve oversight and fairness in policing.

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