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

arXiv · 2502.07677

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

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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

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Param Kulkarni, Yingchi Liu, Hao-Ming Fu, Shaohua Yang, Isuru Gunasekara, Matt Peloquin, Noah Spitzer-Williams, Xiaotian Zhou, Xiaozhong Liu, Zhengping Ji, Yasser Ibrahim. 2025-02-11. Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach. https://arxiv.org/abs/2502.07677

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