Intuition Machines

Cognizers in Complex Human-Technical Assemblages

Authors

  • Linda Kronman University of Bergen (Norway)

DOI:

https://doi.org/10.7146/aprja.v9i1.121489

Keywords:

machine vision, machine learning, nonconscious cognition, datasets

Abstract

The urgency of environmental, security, economic and political crises in the early twenty-first century has propelled the use of machine vision to aid human decision-making. These developments have led to strategies in which functions of human intuitive processing have been externalized to ‘vision machines’ in the hope of optimized and objective insights. I argue that we should approach these replacements of human nonconscious functions as ‘intuition machines.’ I apply this approach through a close reading of artworks which expose the hid- den labour required to train a machine. These artworks demonstrate how human agency shapes the ways that machines perceive the world and reveal how values and biases are hardcoded into nonconscious cognitive machine vision systems. Thus, my analysis suggests that decisions made by such systems cannot be considered fundamentally objective or true. Nevertheless, artworks also exemplify how externalized intuitive processing can still be helpful as long as we refrain from blindly taking the results as a go-signal to take immediate action.

Author Biography

Linda Kronman, University of Bergen (Norway)

Linda Kronman is a media artist, designer and currently a PhD can-didate at the University of Bergen (Norway) researching how machine vision is represented in digital art as a part of the Machine Vision in Everyday Life project. As a part of artist duo KairUs she and has been producing art together with Andreas Zingerle including research topics such as surveillance, smart cities, IoT, cybercrime, online fraud, electronic waste and machine vision.

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Published

2020-08-04