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Excavating “Excavating AI”: The Elephant in the Gallery

Name: Excavating “Excavating AI”: The Elephant in the Gallery
Creator: Lyons, Michael J.
Keywords: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, training sets, Vision, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Social and Behavioral Sciences

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 01 Jan 2020Embargo end date: 01 Jan 2020 English Publisher:Elsevier BVJournal:SSRN Electronic Journal (eissn: 1556-5068,

Authors: Lyons, Michael J.;

Excavating “Excavating AI”: The Elephant in the Gallery

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Abstract

Two art exhibitions, “Training Humans” and “Making Faces,” and the accompanying essay “Excavating AI: The politics of images in machine learning training sets” by Kate Crawford and Trevor Paglen, are making a substantial impact on discourse taking place in the social and mass media networks, and some scholarly circles. Critical scrutiny reveals, however, a self-contradictory stance regarding informed consent for the use of facial images, as well as serious flaws in their critique of ML training sets. Our analysis underlines the non-negotiability of informed consent when using human data in artistic and other contexts, and clarifies issues relating to the description of ML training sets.

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Keywords

FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, training sets, Vision, Computer Science - Artificial Intelligence, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Computer Science - Human-Computer Interaction, Social and Behavioral Sciences, K.4.0, Picture Processing, Human-Computer Interaction (cs.HC), Machine Learning (cs.LG), digital ethics, Computer Science - Computers and Society, Computers and Society (cs.CY), Machine learning, Meta-science, affective computing, Critical theory, Data mining, Computational Neuroscience, Emotion, informed consent, Cognitive Psychology, Information technology--Moral and ethical aspects, 68T01, Engineering Psychology, Affect (Psychology), Artificial Intelligence (cs.AI), machine learning, ai, Perception, Neuroscience

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	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	6
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%