Blinded police-report study distinguishes perceived quality from factual verification

Records supervisors, patrol leadership and legal consumers need reports that communicate reliably. Ask whether current complaints concern omissions, readability or correction work, and whether anyone checks reports against original evidence. A bounded engagement could compare assisted and conventional reports for one incident category using blinded reviewers and source verification. The value hypothesis is a better-informed purchase decision, not guaranteed quality improvement. Explain that the observed accuracy result is a rating, not a counted fabrication rate. Because these reports relate to a previously studied agency, do not present the paper as independent multi-agency replication.

Fit is a drafting copilot with a verifiable evidence path. Map transcription, generation, officer additions, review and RMS export; record model version and intermediate artifacts under approved access controls. Prerequisites include representative authorized recordings and independently checked reference facts. Test missing visual details, speaker attribution, unsupported additions and accessible presentation. Compare total correction effort and reader comprehension alongside factual completeness. Cloud, hybrid and local alternatives require separate data-handling and integration assessment; the preprint validates none of them. Any agent should prepare a draft while an accountable officer retains submission authority.

The records unit should own a limited pilot with training, IT and prosecutor/defender input. Establish baseline quality, recruit independent readers, train supervisors on distinct error types and document reasons for revisions. Dependencies include source access, annotation skills and protected reviewer time. Proposed acceptance requires no critical unsupported facts in the agreed test set, complete provenance, and a locally agreed comprehension and correction-effort target. These are proposed gates, not study results. Recheck after model changes and track non-adoption. Risks include rating fluent text generously, treating a nonsignificant result as equivalence and overlooking burdens shifted to downstream readers.