Literature DB >> 31970596

Four Ironies of Self-quantification: Wearable Technologies and the Quantified Self.

D A Baker1.   

Abstract

Bainbridge's well known "Ironies of Automation" (in: Johannsen, Rijnsdorp (eds) Analysis, design and evaluation of man-machine systems. Elsevier, Amsterdam, pp 129-135, 1983. https://doi.org/10.1016/B978-0-08-029348-6.50026-9) laid out a set of fundamental criticisms surrounding the promises of automation that, even 30 years later, remain both relevant and, in many cases, intractable. Similarly, a set of ironies in technologies for sensor driven self-quantification (often referred to broadly as wearables) is laid out here, spanning from instrumental problems in human factors design (such as disagreement over physiological norms) to much broader social problems (such as loss of freedom). As with automation, these ironies stand in the way of many of the promised benefits of these wearable technologies. It is argued here that without addressing these ironies now, the promises of wearables may not come to fruition, and instead users may experience outcomes that are opposite to those which the designers seek to afford, or, at the very least, those which consumers believe they are being offered. This paper describes four key ironies of sensor driven self-quantification: (1) know more, know better versus no more, no better; (2) greater self-control versus greater social control; (3) well-being versus never being well enough; (4) more choice versus erosion of choice.

Entities:  

Keywords:  Algorithmic responsibility; Self-quantification; Wearable sensors; Wearables

Year:  2020        PMID: 31970596     DOI: 10.1007/s11948-020-00181-w

Source DB:  PubMed          Journal:  Sci Eng Ethics        ISSN: 1353-3452            Impact factor:   3.525


  1 in total

1.  Behavior Change Techniques in Wrist-Worn Wearables to Promote Physical Activity: Content Analysis.

Authors:  Peter Düking; Marie Tafler; Birgit Wallmann-Sperlich; Billy Sperlich; Sonja Kleih
Journal:  JMIR Mhealth Uhealth       Date:  2020-11-19       Impact factor: 4.773

  1 in total

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