Police counted nine wrong face alerts. Out of how many?
Western Australia’s police force published a number that could disappear on a dashboard: 0.00065%. That was the proportion of face scans that produced an alert police classified as incorrect. Imagine looking over the shoulder of the officer who actually received an alert, though. That officer saw one of 292 alerts, not one of 1,380,882 scans. Nine incorrect alerts out of 292 is about 3.1%. The numerator never moved. The question did. The trial ended October 31 after 100 deployments at 43 locations. Police reported 122 arrests during deployments and said officers reviewed potential matches. A scan isn’t a unique individual, an alert isn’t an arrest and an arrest isn’t a conviction. Nor can either percentage tell us how often the system failed to notice someone on the watchlist. Calculating that would require knowing how many such people passed the cameras, including the ones the cameras missed. A separate US paper offers a useful way to think about who gets counted before technology reaches the street or the home. Researchers reviewed documents describing 100 projects funded by the National Institute on Aging. Few explicitly described involving end users in design or targeting underserved populations. That is evidence about what the project documents recorded. It does not establish that patients were harmed, and the researchers did not evaluate the Australian cameras. The common question is whose experience the chosen measure leaves out. For a person wrongly approached in public, the outcome is the encounter, not a near-zero percentage on a slide.
