Flock Safety's automated license plate readers are mistakenly flagging a 76-year-old Colorado woman as a suspect due to a zero/O mix-up in a police database. Every time she drives, her valid plate triggers alerts because a suspect’s plate was entered incorrectly—replacing a zero with the letter O. The system, designed to catch stolen vehicles, now sends officers to pull her over despite her innocence.**

The error stems from human data-entry flaws, not the technology itself. Flock Safety’s cameras read plates correctly but match them to a corrupted database. This isn’t an isolated issue: after a similar case in Cherry Hills surfaced, multiple Coloradans reported being wrongly flagged. None have clear avenues to clear their names, highlighting a lack of accountability in automated policing tools.**

Public outrage has grown, with critics calling for policy reforms to ensure error correction protocols and transparency. One commenter demanded to know who would compensate those wrongly targeted. The woman’s plight underscores systemic risks when databases outpace oversight—innocent people face bureaucratic hurdles to prove their innocence.**

Experts urge immediate fixes: audits of license plate data, streamlined dispute processes, and agency responsibility for false alerts. Until then, the next person pulled over for a database typo could be anyone. Systemic flaws, not individual mistakes, are the real culprit here.