The collection "Algorithmic Governmentality," edited by Maurice Erb and Simon Ganahl, deals with the hypothesis of "algorithmic governmentality," which impels us to examine recent advances in automated computing (i.e., machine learning) from a Foucauldian perspective. Governmentality is understood here in the sense of the lecture series at the Collège de France in 1978/79, where Foucault further developed his relational concept of power. The blend of "algorithmic governmentality" points to the possibility of governing users-consumers' future behaviors by exploiting past information on them. Such traces (e.g., locations, clicks, scrolling), when massively collected, are now used to algorithmically build predictive models for anticipating subsequent activities. Once they are up and running, these predictions serve to refine existing marketing policies. The novelty of such techniques rests partly in the possibility of instantaneously tailoring an interface to the experience of a singular user-consumer. In order to explore these digital practices, the special collection lays the focus on recommender systems and algorithmic finance.
Research
Algorithmic Finance and (Limits to) Governmentality: On Foucault and High-Frequency Trading
Christian Borch
2017-09-01 Volume 3 • Issue 1 • 2017 • 1–17
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Recommender Systems as Techniques of the Self?
Tyler Reigeluth
2017-09-01 Volume 3 • Issue 1 • 2017 • 1–25
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Beyond Surveillance: How Do Markets and Algorithms "Think"?
Bernhard Rieder
2017-09-01 Volume 3 • Issue 1 • 2017 • 1–20
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Algorithmic Decision-Making, Spectrogenic Profiling, and Hyper-Facticity in the Age of Post-Truth
Richard Weiskopf
2020-03-09 Volume 6 • Issue 1 • 2020 • 1–37
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Genealogy of Algorithms: Datafication as Transvaluation
Virgil W. Brower
2020-10-19 Volume 6 • Issue 1 • 2020 • 1–43
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