Abstract
Based on detailed descriptions of human–machine ensembles, this article explores how humans and machines work together to see specific things and unsee others, and how they come to co-configure one another. For seeing is not an automated function; whether one is a human or a machine, vision is gradually enskilled and mutually co-constituted. The analysis intersects three different ways of human–machine seeing to shed further light on the workings of each one: an airport, where facial recognition algorithms collaborate with border guards to grant passage to particular travellers and not to others; a luggage-scanning system, where potential security threats are assessed by a complex of X-rays and human intro-spection; and a hospital operating room, where human–machinic surgical robots find their way and operate on the insides of human bodies, touching only by seeing. In these examples, human and machine ways of seeing merge together, seeing in particular apparatuses of material, political, organisational, economic and fleshy components. The article analyses the practical work of human–machinic collaboration and explores how the different material and social constituents, not necessarily always working from the same agenda, come to configure what can be seen and sensed and what cannot.
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Notes
My own research projects have generally been based on the practical, intersubjective and epistemological aspects of working with and creating anthropological knowledge through a camera, as well as the particular ways in which the filming process configures and frames one’s approach to and interactions with the world (Møhl 1997, 2011, 2012; Møhl and Kristensen 2018).
The term “haptic vision” is also sometimes used in the inverse sense to describe the capacity to see an object by touching it, a common example being the blind person. This does not, however, interfere with the project of dissolving the distinctions between the senses—on the contrary.
Diffraction, a term from classical physics, describes the effect of waves, e.g. light waves, being bent when they hit an obstacle. Different wavelengths are not bent to the same degree, resulting in a particular light pattern. What we see are not the differences in wavelengths but the effects of those differences when they are curved around an obstacle. As Haraway notes, “A diffraction pattern does not map where differences appear, but rather maps where the effects of difference appear.” (Haraway 1992:300).
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Møhl, P. Seeing threats, sensing flesh: human–machine ensembles at work. AI & Soc 36, 1243–1252 (2021). https://doi.org/10.1007/s00146-020-01064-1
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DOI: https://doi.org/10.1007/s00146-020-01064-1