What Happens When Face Recognition Tech Can't Tell Black People Apart

Our Take

Credit to the ACLU for keeping Robert Williams' case in front of new audiences. Getting arrested on your own lawn, in front of your kids, because an algorithm decided your old driver's license photo looked enough like a blurry shoplifting still — that's not an edge case, it's a preview of what happens when we let machines make accusations that used to require a human witness who could be cross-examined.

We cover ALPR networks instead of face recognition, but the pattern is identical: opaque matching systems, minimal human review, and error rates that fall hardest on Black and brown communities. Flock's cameras aren't scanning faces, but they are building location histories on every driver who passes a reader, feeding those histories into shared databases that police departments and, increasingly, ICE and other agencies can query. A bad match on a plate can trigger the same kind of felony-stop nightmare Williams lived through — guns drawn over a algorithmic guess.

The lesson from Robert Williams' story isn't just about one bad technology, it's about what happens when we let any surveillance system operate without transparency, audits, or accountability. Check our camera map to see how dense this tracking has gotten in your area, and hit take action if you want to push back before the next wrongful arrest happens in your town.