The disciplinary power of predictive algorithms: a Foucauldian perspective

Paul B. de Laat*

*Corresponding author for this work

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Abstract

Big Data are increasingly used in machine learning in order to create predictive models. How are predictive practices that use such models to be situated? In the field of surveillance studies many of its practitioners assert that "governance by discipline" has given way to "governance by risk". The individual is dissolved into his/her constituent data and no longer addressed. I argue that, on the contrary, in most of the contexts where predictive modelling is used, it constitutes Foucauldian discipline. Compliance to a norm occupies centre stage; suspected deviants are subjected to close attention-as the precursor of possible sanctions. The predictive modelling involved uses personal data from both the focal institution and elsewhere ("Polypanopticon"). As a result, the individual re-emerges as the focus of scrutiny. Subsequently, small excursions into Foucauldian texts discuss his discourses on the creation of the "delinquent", and on the governmental approach to smallpox epidemics. It is shown that his insights only mildly resemble prediction as based on machine learning; several conceptual steps had to be taken for modern machine learning to evolve. Finally, the options available to those subjected to predictive disciplining are discussed: to what extent can they comply, question, or resist? Through a discussion of the concepts of transparency and "gaming the system" I conclude that our predicament is gloomy, in a Kafkaesque fashion.

Original languageEnglish
Pages (from-to)319-329
Number of pages11
JournalEthics and Information Technology
Volume21
Issue number4
Early online date23-Jul-2019
DOIs
Publication statusPublished - Dec-2019

Keywords

  • Algorithms
  • Discipline
  • Foucault
  • Machine learning
  • Normation
  • Panopticon
  • Prediction
  • Risk
  • Transparency

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