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

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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Published
2025-05-08
DOI
https://doi.org/10.1145/3701716.3715166
Primary Topic
Artificial Intelligence in Law
Type
article
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Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered LLM Approach

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

Prathamesh Kulkarni, Yingchi Liu, Noah Spitzer-Williams, Xiaozhong Liu, Shuang‐Hua Yang, Xiaotian Zhou, Yasser Ibrahim, Zhengping Ji, Isuru Gunasekara, Matt Peloquin, Hao-Ming Fu
article en

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

Worcester Polytechnic Institute (US)
Openalex Percentile: Top 87%
Artificial Intelligence in Law
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