By João Carlos Rebello Caribé

This paper was presented at II Seminário Internacional Network Science in November de 2018 at Rio de Janeiro.

Full translated article is on the way, please return to this page soon, or follow me at Researchgate.


When it comes to surveillance, then comes the mind the image of a camera, an observer behind the monitors. It is a model naturalized in the twentieth century, which with the advent of the big date, is becoming obsolete.

The surveillance model of Benthan’s Panopticon, described by Foucault (2014) in “Vigiar e Punir”, is based on the see-seen pair, from a central observation point, with the vigilant having ample vision of the watched and this no view of the watcher, thus assuming his watchfulness.

With the emergence of mobility and surveillance capitalism (Shoshana Zuboff, 2015), new systematizations of surveillance models have emerged. Zigmunt Bauman (2013) in “Vigilância Líquida”, presents the personal Panopticon model, where the individual becomes vigilant of himself and his peers, carrying his own Panopticon, materialized as his smartphones and connected devices. What Bauman describes, dialogues with what Fernanda Bruno (2013), in “Máquinas de ver, Modo de Ser. Vigilância Tecnologia e Subjetividade” describes as Distributed Surveillance, which takes away the centrality of surveillance, main characteristic of the Panopticon.

Sandra Braman (2006) in the book “Change of State – Information, Policy, and Power” presents Panspectron as the model of surveillance appropriate to the advent of the big data. According to the author, the focus of Panspectron is not the individual in particular, its focus is on the data, and its focal action is in response to patterns.

The volume of data produced voluntarily and involuntarily, by the individual, the Internet make up the new oil, Facebook, for example, had gross revenues of $40.6 billion in 2017, Alphabet, Google holding, grossed $110 billion , in the same period.

Panspectrons, trained with models, through machine learning, build from there, through deep learnig, sophisticated patterns that respond in a lateral way, distinct from human logic, with extreme accuracy to questions asked in the panspectral screen. Yoyou Wu et al (2015) demonstrates in “Computer-based personality judgments are more accurate than those made by humans” as computer-based judgments are more accurate than those made by humans.

The individual’s digital footprints produce valuable information about their individuality, preferences, fears, and even reveal their more intimate secrets.


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