Datafied Brains and Digital Twins: Lessons From Industry, Caution For Psychiatry

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Abstract

This paper asks what sorts of ethical caution ought to attach to increasingly data-driven approaches to understanding the brain. This is taken to be an important question especially owing to a likely near future of neuromonitoring and neuromodulation devices with applications in psychiatry. The paper explores this by i) sketching the concept of ‘digital twin,’ ii) drawing a schematic picture of ‘brain datafication’ in general, and iii) developing a means of understanding some challenges present in datafication through the lens of digital twins. One central concern arises from the role algorithmic processing of neural recordings plays in terms of neuroscientific objectivity, with knock on effects for psychiatric ethics. Essentially, this is owing to a way in which algorithmic processing in brain data construction appears to be deductive in character, but is in fact based on a particular scheme of inductive inference. The challenges explored urge ethical caution as they concern epistemological gaps in data-centered neuroscientific progress, as well as knock-on effects for psychiatry.

Original languageEnglish
Pages (from-to)29-42
Number of pages14
JournalPhilosophy, Psychiatry and Psychology
Volume29
Issue number1
DOIs
Publication statusPublished - 2022

Keywords

  • algorithms
  • Brain data
  • deep learning
  • digital twin
  • ethics
  • neurorecording
  • psychiatry

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