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      The deception of an infinite view – exploring machine vision in digital art

      Politics of the Machine Beirut 2019 (POM2019)

      Politics of the Machine

      11-14 June 2019

      Digital Art, Machine Vision, Geospatial Intelligence, Machine Learning, Aerial Photography

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          Abstract

          This paper examines prediction product called Queryable Earth a project to “make Earth searchable for all”. A project pitched by the company Planet, owner of the largest fleet of Earth-imaging satellites in orbit and an archive of satellite images growing with terabytes of fresh data every day. The aim of Queryable Earth is to combine geospatial intelligence with machine learning. By training artificial neural networks to classify objects, identify geographic features, and monitor change over time, the implied intention is to create a predictive, omniscient oracle. In this paper Queryable Earth functions as an example of a ‘nonconscious cognitive assemblage’ combining aerial image with machine learning techniques such as artificial neural networks. To examine the predictive potential and the assumed objectivity of machine vision systems such as Queryable Earth I turn to histories of aerial photography and examples of contemporary digital art to illustrate how human and technical cognition entwine revealing how seemingly automated processes such as rendering of satellite images and pattern recognition still inherit human biases and are prone to emphasize them. Furthermore, I use digital artworks to illustrate how Queryable Earth as an “all seeing machine” is limited to a singular aerial perspective which cannot penetrate the surface and how predictions produced by such systems are constrained the quality and selection of data they are trained on.

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          Most cited references 21

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          AI can be sexist and racist — it’s time to make it fair

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            Computer science: The learning machines.

             Nicola Jones (2014)
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              Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification

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                Author and article information

                Contributors
                Conference
                June 2019
                June 2019
                : 70-77
                Affiliations
                University of Bergen

                Sydnesplassen 7, Bergen, Norway
                Article
                10.14236/ewic/POM19.11
                © Kronman. Published by BCS Learning and Development Ltd. Proceedings of POM Beirut 2019

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Politics of the Machine Beirut 2019
                POM2019
                2
                Beirut, Lebanon
                11-14 June 2019
                Electronic Workshops in Computing (eWiC)
                Politics of the Machine
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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