Roberge, Jonathan 
ORCID: https://orcid.org/0000-0001-9282-5218; Morin, Kevin et Senneville, Marius
(2020).
Deep Learning’s Governmentality: The Other Black Box
In:
The Democratization of Artificial Intelligence: Net Politics in the Era of Learning Algorithms.
Transcript-Verlag, Bielefeld, pp. 123-142.
Résumé
Frank Pasquale’s 2016 book The Black Box Society is now considered a landmark study in law-related disciplines, in the social sciences and beyond. The topic in itself, digitalization and the invasiveness of the internet, is of the utmost impor- tance. The book reveals the inversion of operational secrecy by digital platforms and the extensive access to users’ private data. Facebook, Google and the like collect and aggregate bits and bytes of information to create massive profiling schemes, the modus operandi of which little is known. Problematically, as private actors, they acquire massive data and “knowledge” about the society and our in- dividual behaviors that we don’t. To that complex and most certainly unpleasant reality, Pasquale’s analytical rigor and finesse contribute valuable insights on po- tential regulations and on the possibility of developing a smarter citizenry. How- ever, we want to argue that there is another, broader and maybe more cultural reason why The Black Box Society draw so much attention—thus further explaining the success of the book. The image of a concealed, networked entity is evocative of some common fears. It captures a sense of “loss of control” vis-à-vis the latest au- tomation processes. Such an algorithmic black box, in other words, taps into a dif- fused anxiety regarding what is to be called a “known-unknown”, i.e. something we recognize to be a mostly hidden form of knowledge production. The image of a black box is a disenchanted one; here lies its strength as well as its weakness.
| Type de document: | Chapitre de livre |
|---|---|
| Mots-clés libres: | artifical intelligence; gouvernmentality |
| Centre: | Centre Urbanisation Culture Société |
| Date de dépôt: | 04 nov. 2020 19:45 |
| Dernière modification: | 07 oct. 2026 18:24 |
| URI: | https://espace.inrs.ca/id/eprint/10470 |
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