Intra-actions in data-driven systems: A case study in creative praxis

Chris Speed, Martin Disley

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)peer-review

Abstract

This chapter seeks to identify a criticality for creative praxis that uses artificial intelligence within the production of a series of artworks. The article refers to the work of Karen Barad to provide a theoretical framework within which to understand how machine learning and deep learning support intra-actions between humans (artist, data scientist, audience), data sets, and algorithms in the production of artwork. By recovering the work of McQuillan who calls for Agential Realism to inspire a countercultural praxis for data science, and Joler and Pasquinelli who present the limitations of datasets as a resource for creative praxis, the authors identify the logical and political limitations of AI to predict or generate something statistically unlike the already existing or the already known. A case study is introduced to exemplify the implications upon creative praxis that involves working with data-driven technologies.
Original languageEnglish
Title of host publicationDistributed Perception
Subtitle of host publicationResonances and Axiologies
EditorsNatasha Lushetich, Iain Campbell
PublisherRoutledge
Chapter11
Edition1
ISBN (Print)9780367743017
Publication statusAccepted/In press - 21 May 2021

Publication series

NameRoutledge Studies in Science, Technology and Society

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