“Strongly recommended” revisiting decisional privacy to judge hypernudging in self-tracking technologies

M. Lanzing (Corresponding author)

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This paper explores and rehabilitates the value of decisional privacy as a conceptual tool, complementary to informational privacy, for critiquing personalized choice architectures employed by self-tracking technologies. Self-tracking technologies are promoted and used as a means to self-improvement. Based on large aggregates of personal data and the data of other users, self-tracking technologies offer personalized feedback that nudges the user into behavioral change. The real-time personalization of choice architectures requires continuous surveillance and is a very powerful technology, recently coined as “hypernudging.” While users celebrate the increased personalization of their coaching devices, “hypernudging” technologies raise concerns about manipulation. This paper addresses that intuition by claiming that decisional privacy is at stake. It thus counters the trend to solely focus on informational privacy when evaluating information and communication technologies. It proposes that decisional privacy and informational privacy are often part of a mutually reinforcing dynamic. Hypernudging is used as a key example to illustrate that the two dimensions should not be treated separately. Hypernudging self-tracking technologies compromise autonomy because they violate informational and decisional privacy. In order to effectively judge whether technologies that use hypernudges empower users, we need both privacy dimensions as conceptual tools.
Original languageEnglish
Pages (from-to)549-568
Number of pages20
JournalPhilosophy & Technology
Issue number3
Early online date7 Jun 2018
Publication statusPublished - 15 Sept 2019


  • Ethics of Privacy
  • Autonomy
  • Manipulation
  • Hypernudging
  • Nudging
  • Big Data
  • Personalisation
  • Decisional Privacy
  • Informational Privacy
  • Self-Tracking
  • Self-tracking
  • Informational privacy
  • Ethics of privacy
  • Surveillance
  • Personalized feedback
  • Big data
  • Decisional privacy


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