AI-Assisted Police Report Writing

Draft One transcribes the audio from an officer's body-worn camera and generates a first draft of the incident report. Axon launched it in April 2024 and describes it as its fastest-growing product. It is built on OpenAI's GPT-4, with what one account describes as the creativity setting turned down to keep output tied to what was actually recorded.

It matters more than most tools in this catalogue because of where police reports sit. A report is the account an officer gives of what happened and why they acted; it informs whether a prosecutor charges, whether a judge grants bail, and what an officer relies on when testifying, sometimes years later. Changing how that document is produced changes an input to the whole criminal process.

Axon is not alone — Truleo sells a comparable product called Field Notes — but Axon's position matters: it is the largest supplier of body-worn cameras in the United States, supports more than 5,000 police departments, and Draft One integrates directly with the camera and evidence systems those departments already run.

WHAT IT DOES, AND WHAT FORCES SAY ABOUT IT

The time saving is the whole pitch, and the figures are striking. Scottsdale Police, running a pilot, reported saving at least thirty minutes of officer time per report. In Oklahoma City, an hour-long search with a K-9 unit was transcribed and summarised into a report in eight seconds, against thirty to forty-five minutes to write by hand.

Fort Collins in Colorado offered the clearest adoption account: a technology sergeant tested it after a demo, said he was struck by the quality and speed, initially made it available to around seventy officers, and later extended it to all of them. Other named deployments include Lafayette in Indiana, Tampa in Florida, and Campbell in California.

Forces consistently describe human review as the control. A Scottsdale sergeant emphasised that getting details right matters for courts, records and prosecution, and that the tool is not bringing in content that was not there in the first place.

WHERE IT HAS GONE WRONG

The most quoted failure is also the clearest illustration of the risk. In Heber City, Utah, an AI-generated police report stated that an officer turned into a frog partway through responding to an incident. Nothing in the audio supported that; the model produced it.

A frog is obvious and gets caught. The concern is the class of error that is plausible enough not to be.

The more systematic problem is what investigative reporting found about how departments configure the tool. Axon builds in transparency features — disclaimers marking a report as AI-assisted, and settings requiring officer input before submission. Of seven departments responding to records requests, only South Jordan in Utah maintained mandatory officer input, and even there officers could bypass it. Departments in Lafayette and Fort Collins were reported to have intentionally turned the transparency defaults off.

Worse, some police chiefs did not know which of their reports had been AI-drafted at all, because officers were copying the generated narrative into reports they then indicated they had written themselves — bypassing every disclaimer and audit trail the system provides.

Most departments do not restrict use by offence type. South Jordan alone generated more than 900 AI-assisted reports across a range of incidents, including serious felonies.

One detail from Campbell, California, suggests the tool changes the documents themselves rather than merely producing them faster. The department's records supervisor emailed staff noting a significant difference in narrative format, and asked to be told if the District Attorney's office had comments about it.

THE REGULATORY RESPONSE

This is one of the few technologies in this catalogue where legislatures moved quickly.

Utah's SB 180 requires that any police report created wholly or partly by generative AI carry a disclaimer saying so. On 10 October 2025 California became the second state to act, when Governor Newsom signed SB 524. That law goes further: it requires AI-assisted reports to be marked as such, requires agencies to keep an audit trail identifying who used the AI and which audio and video was used, and requires the first AI-generated draft to be retained for as long as the official report.

Separately, the King County prosecuting attorney's office in Washington State barred police from using AI to write reports at all — a prosecutor, rather than a legislature, declining to accept the output.

THE CASE FOR IT

Report writing genuinely consumes a large share of officer time, and time spent typing is time not spent on other duties. If the saving is anywhere near thirty minutes per report, across a shift and a force, that is substantial.

Transcription from a recording also has a real accuracy argument in its favour: a report written from body-camera audio hours later, from memory, is a worse record than one drafted from what the microphone actually captured. Used properly, the tool anchors the narrative to the recording.

Axon's own disclaimer and officer-input features are the right controls to have built. The failure documented above is that departments switched them off, not that they were absent.

THE CASE AGAINST

Generative models produce fluent text regardless of whether it is accurate, and fluency is precisely what makes an error hard to spot in a document a reviewer expects to be routine. The frog is memorable because it is absurd; a subtly wrong sequence of events, or an inferred intention nobody stated, would read perfectly.

The bundling concern is real. Axon supplies the cameras, the evidence storage and now the report generator, and civil liberties groups have argued that bundling makes it easy for departments to acquire more technology than they need or than the public has agreed to.

There is also a training-data question that nobody has answered publicly: a model that learns from past police reports learns the conventions, framing and any bias embedded in them.

Finally, the audit problem. Where transparency defaults are disabled and officers paste output into reports they sign as their own, it becomes impossible after the fact to establish which reports were machine-drafted — which forecloses any later study of whether the technology is accurate. Axon's own SEC filings acknowledge that AI failures could create operational and legal challenges and that datasets may carry bias.

WHAT IS NOT ESTABLISHED

Axon has declined to say how many departments use Draft One, so the true scale is unknown.

No independent evaluation of the accuracy of AI-drafted reports against officer-written ones has been identified.

Whether the claimed thirty-minute saving holds outside vendor and department accounts has not been independently tested; the EFF has questioned it directly.

How prosecutors and courts treat an AI-assisted report where disclosure was not made is untested in any reported case reviewed here.