Flock sent 1,427 alerts to Roseville police over two years, flagging cars as stolen or used in a felony. In 71% of them, Flock read the plates wrong.

In 2023 and 2024, Flock sent 1,427 alerts to the Roseville [CA] Police Department.

Roseville police found that Flock misread license plates in 71% of those alerts.

Max Isaacs, director of technology law and policy with the Policing Project at New York University’s School of Law, described Roseville’s misread data as “stunning.”

“A 71% misread rate is really beyond the pale,” he told Business Insider, referring to Roseville’s reported error rate for stolen and felony vehicle alerts. He added that “in the absence of concrete rules that directly address the problem of misreads, this technology is not safe to be deployed in communities.”

  • Wildmimic@anarchist.nexusEnglish
    2·
    6 hours ago

    It’s basic statistics.

    • True Positives (TP) 415
    • False Positives (FP) 1,012
    • False Negatives (FN) (Calculated using 96% Recall) 17 Target plates that were missed.
    • Precision TP / (TP + FP) 29.1% When it hits, it’s wrong most of the time.
    • Recall (Assumed from Global Acc.) 96.0% It successfully catches almost all targets.

    That’s the problem if you are fishing for a very small number of targets out of an absolutely bonkers large pool of potential targets - at some point your false positives start becoming more and more prevalent in relation to actual positives. That is simply statistics and if flock can’t improve the global accuracy by some exponents, this problem will grow - the more Flock gets used and the more licence plates they are scanning, the more the false positives increase.

    And it is a massive problem, because the false positive marks you as a criminal - that means any legal recourse gets expensive and painful. This technology creates more problems for everyday people than the amount of issues it removes by tracking actual criminals.

    Cutting them down with a reciprocating saw is absolutely justified. These things are an affront to privacy and accuse random citizens of being guilty of felonies.