Wrongfully Arrested by a Computer: The Pattern Nobody Talks About!
The short version: The Black Post lays out the MIT/NIST data on facial recognition's racial bias, a reminder that every automated ID system — plates included — carries error rates that fall hardest on Black communities.
Our Take
Credit to The Black Post for pulling the NIST and MIT numbers together in one place: facial recognition systems misidentifying Black faces at 10 to 100 times the rate of white faces isn't a rumor, it's documented federal research. That distinction matters because law enforcement agencies keep deploying these tools as if they were neutral math instead of software trained on skewed data and skewed policing history.
We cover ALPRs specifically, but the pattern the video describes is the same one we see with license plate readers: a computer generates a "match," a cop treats it as probable cause, and a human being gets stopped, cuffed, or worse before anyone checks the work. Flock's own marketing leans on the same promise of algorithmic certainty that facial recognition vendors sell, and the same lack of independent audit trails. Wrongful stops tied to bad plate reads aren't hypothetical — they're a predictable output of scaling automated suspicion across neighborhoods that are already over-policed.
If a video like this makes you want to know how thick the surveillance net actually is where you live, check our camera map to see documented Flock locations near you, and visit our take-action page for ways to push back on your city council before the next contract renewal.
This is DeFlock The USA’s original commentary. The video above is the work of The Black Post, published on YouTube — full credit to the creator.