The global labours of AI and data intensive systems

Benedetta Catanzariti, Srrayva Chandhiramowuli, Suha Mohamed, Sarayu Natarajan, Shantanu Prabhat, Noopur Raval, Alex S. Taylor*, Ding Wang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The remarkable capacities of AI-infused and data intensive systems are regularly presented as emblematic of technoscientific innovation and progress. Far less recognised is the human labour required to sift through and sort data, and ultimately train these systems. This is a labour distributed well beyond the presumed centres of technological innovation, and spun into and strewn across geographically distributed regions in the ”Global South”. This workshop brings together scholars and practitioners interested in and conducting research on the hidden labours that lie behind AI and data intensive systems. Participants will share perspectives or results of their research and, through highly exploratory engagements and dialogues, set out a critical mode of inquiry for future directions in research, design and policy.
Original languageEnglish
Title of host publicationCSCW '21
Subtitle of host publicationCompanion Publication of the 2021 Conference on Computer Supported Cooperative Work and Social Computing
EditorsJeremy Birnholtz, Luigina Ciolfi, Sharon Ding, Susan Fussell, Andrés Monroy-Hernández, Sean Munson, Irina Shklovski, Mor Naaman
PublisherAssociation for Computing Machinery (ACM)
Pages319-322
Number of pages4
ISBN (Electronic)9781450384797
DOIs
Publication statusPublished - 23 Oct 2021
EventThe 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing -
Duration: 23 Oct 202127 Oct 2021
https://cscw.acm.org/2021/

Conference

ConferenceThe 24th ACM Conference on Computer-Supported Cooperative Work and Social Computing
Period23/10/2127/10/21
Internet address

Keywords / Materials (for Non-textual outputs)

  • speculative futures
  • content moderation
  • data labeling
  • labour
  • gig economy

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