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 / Description of output

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
Place of PublicationLondon
Number of pages15
ISBN (Electronic)9781003157021
ISBN (Print)9780367743017
Publication statusPublished - 30 Dec 2021

Publication series

NameRoutledge Studies in Science, Technology and Society


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