Transparency compels conformity and erases deviation

Evidence

For Nietzsche, the figures of statistics prove only that man is a herd animal. He observes that ‘die Menschen zunehmen im Gleichwerden’. This means both that ‘people are increasingly growing equal’ and that ‘people batten [i.e., grow fat] as they become the same.’ Such Gleichschaltung also characterizes our contemporary society of transparency and information. When everything is made visible at once, deviations can scarcely occur. Transparency entails a compulsion to conform, which eliminates the Other, the Alien and the Deviant. Above all, Big Data makes collective patterns of behaviour visible. Dataism is augmenting Zunehmen im Gleichwerden, or hypertrophied sameness. Data-mining does not differ from statistics in any fundamental way. The correlations it lays bare represent only what is likely in statistical terms. It calculates average values. As such, Big Data has no access to what is unique. Big Data is wholly blind to the event. Not what is statistically likely, but what is unlikely – the singular, the event – will shape history, in other words, the future of mankind. Thus, Big Data is blind to the future too. — Byung-Chul Han, Psychopolitics, 176

Categorization

  • XREF: Connects to Han’s broader ‘shame society’ vs transparency arguments in this book and to contemporary critiques of surveillance-adjacent ‘radical transparency’ in tech. — connects to: Transparency can function as a mechanism of control, not liberation — , The Braided Reading EPUB: 5-Year…, 1259; Transparency functions as a deterrent against political corruption and misconduct. — Russell Muirhead and Nancy L. Rosenblum, A Lot of People Are Saying: The New…, 316; Anti-conformity movements reproduce the conformity they reject — Georg Simmel, on individuality and social forms_…, 728 (+3 more)
  • DEFINE: Exploits the ambiguity of Nietzsche’s phrase Gleichwerden as both growing equal and growing fat — single word carrying a double critique.
  • SEED: Angle: data-mining is continuous with classical statistics, so its epistemic limits (blindness to the singular event and to the future) are old problems, not new ones Big Data solving.

Broader Connections