Stream your brain! Speculative economy of the IoT and its pan-kinetic dataveillance. Issue 2 (July 2021)

• Record Type: Journal Article
• Authors: Ahn, Sungyong
• Is Part Of: Big data & society. Volume 8:Issue 2(2021)
• Publication Date: 2021-07
• Subjects: Internet of things -- speculative realism -- dataveillance -- panoptics -- pankinetics -- electroencephalogram; Big data -- Social aspects -- Periodicals; Social sciences -- Research -- Data processing -- Periodicals; Social sciences -- Research -- Methodology -- Periodicals; Data mining -- Periodicals; 300.28557
• DOI: 10.1177/20539517211051973
• Languages: English
• ISSNs: 2053-9517

Abstract

It is now a common belief that the truths of our lives are hidden in the databases streamed from our interactions in smart environments. In this current hype of big data, the Internet of Things has been suggested as the idea to embed small sensors and actuators everywhere to unfold the truths beneath the surfaces of everything. However, remaining the technology that promises more than it can provide thus far, more important for the IoT's actual expansion to various social domains than the actual discovery of hidden truths has been people's speculations about the unknown problems, such as hidden security issues or lifestyle concerns, beyond the narrow human knowability but assumed to leave their traces in the IoT-collected big data. This paper discuss this speculation as the concealed cognitive labor of IoT users that projects some fictitious values to the big data IoT companies accumulate. By the term pan-kinetics, the systemic operation of smart actuators is analyzed as the process through which fictitious values of data are converted to the real values as these actuators draw some profitable correlations from physical domains of the IoT. Analyzing smart electroencephalogram (EEG) headsets as the unique IoT devices operating on human brains, it argues how the IoT translates this speculative realism of unknown problems into its big data, which the IoT developers believe to be full of machine-learnable correlations that would lead to the smart solutions of the problems.