In a test at one station, Transport for London used a computer vision system to try and detect crime and weapons, people falling on the tracks, and fare dodgers, documents obtained by WIRED show.
Thousands of people using the London Underground had their movements, behavior, and body language watched by AI surveillance software designed to see if they were committing crimes or were in unsafe situations, new documents obtained by WIRED reveal. The machine-learning software was combined with live CCTV footage to try to detect aggressive behavior and guns or knives being brandished, as well as looking for people falling onto Tube tracks or dodging fares.
“The training data is always insufficient because these things are arguably too complex and nuanced to be captured properly in data sets with the necessary nuances,” Leufer says, noting it is positive that TfL acknowledged it did not have enough training data. “I'm extremely skeptical about whether machine-learning systems can be used to reliably detect aggression in a way that isn’t simply replicating existing societal biases about what type of behavior is acceptable in public spaces.