VeriPol AI Report Analysis

VeriPol analysed the written text of a robbery report — what a victim told police — and flagged characteristics statistically associated with false reporting. It is a materially different kind of police AI from the video and biometric systems that dominate this catalogue: it analysed language in a written account rather than an image or a biometric sample, and its subject was the complainant rather than a suspect.

It was launched nationally in October 2018 following a 2017 pilot in Malaga and Murcia, promoted at the time as more than 90% accurate. Spain's Directorate General of Police confirmed to the transparency organisation Civio that the tool stopped being used on 21 October 2024.

THE EVIDENCE BASE WAS THIN FROM THE START

The model was trained on 1,122 robbery reports from 2015. That is a small corpus for a tool deployed nationally, and analysis by the University of Valencia identified serious design deficiencies.

Usage collapsed well before withdrawal: from roughly 84,000 reports analysed by October 2020 to 3,762 in 2022.

WHY IT WAS WITHDRAWN

The timing is the most informative fact. The tool was withdrawn three months after the EU AI Act came into force, and this is widely reported as a factor, since the Act classifies tools of this kind as high risk and imposes obligations accordingly.

The Interior Ministry has declined to release which police stations deployed it or how often, and a transparency complaint on that point remains open.

THE CASE FOR IT

False reporting is a real phenomenon that consumes investigative resource, and identifying it earlier would in principle free capacity for genuine victims.

Analysing text is also a less intrusive proposition than biometric identification: no image is captured, no database is searched, and no innocent third party is processed.

THE CASE AGAINST

The objection specific to this tool is what it did to the relationship between police and complainants. A system that scores a victim's account for probable falsity, applied at the point of reporting, risks discouraging reports and directing suspicion at people seeking help.

That risk is not evenly distributed. Under-reporting is already a documented problem in categories of crime where victims fear disbelief, and a tool trained to identify statistically improbable accounts will tend to flag accounts that differ from the norm — which is not the same as accounts that are false.

The training corpus of 1,122 reports cannot support a 90% accuracy claim applied nationally to a diverse population, and the University of Valencia's findings reinforce that.

Finally, the transparency record. The tool ran for six years and the Ministry will not say where it was used or how often, which means no one can assess how many complainants were affected.

WHAT IS NOT ESTABLISHED

Which police stations used the tool, and how many reports were assessed in total, remains undisclosed and subject to an open transparency complaint.

What happened to complainants whose reports were flagged is not documented.

Whether any report flagged by VeriPol was later shown to be genuine is not published.

The basis for the original 90% accuracy claim, beyond the 1,122-report training set, is not established.