mlsanityData to 14 Oct 2026

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

arXiv 2502.07677 ↗First posted 11 Feb 2025cs.CLcited by 0

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Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 2 of its 11 references that are in the map. First posted on arXiv 11 Feb 2025.

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References · 2

  1. 2023Precedent-Enhanced Legal Judgment Prediction with LLM and Domain-Model Collaborationcited by 52
  2. 2024Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learningcited by 12

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