Detecting the watchers

Detecting the watchers

Surveillance has been the big idea of criminology for some time. It appears to be a ubiquitous fact of life in modern societies and an organising principle present in most walks of life. It is present in the lives of call centre workers and bus passengers. It is the go to for any attempt at civilising a location.

Critically, surveillance assemblages modify post-hoc rationalities as well as they produce targeting and risk prediction matrixes. They shift the mode of surveillance from the human to the non-human and automated, and from the organisational to the intimate. People are rewarded for participation in an implicit social credit system. The system makes profiling ordinary (e.g. Muslims in Japan are risk profiled by the police). That changes how we understand forensic data for example, it’s no longer post but is no pre- event (Mantello, 2016). In some ways this is old school surveillance with many data layers but the main innovation is its capacity to produce target selection. Doing so collapses key distinctions eg. between present and future culpability, hence the ‘pre crime’ label.

There are plenty of private sector examples to draw from. One, the Hitachi visualisation suite, gives some good instances of how this might work. It overlays social media analytics, dispatch info, gunshot sensors, CCTV with info about weather and traffic. As the sensing infrastructure becomes more developed this will only be added to. Picture a live feed from every Ring video doorbell with AI analytics. We shouldn’t be too tech led in assuming the whole concept of the disciplinary society needs to be remade.

Mantello P (2016) The machine that ate bad people: The ontopolitics of the precrime assemblage. Big Data & Society 3(2): 2053951716682538. DOI: 10.1177/2053951716682538.
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