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dc.contributor.authorNewlands, Gemma Elisabeth Marjorie
dc.date.accessioned2023-01-04T14:59:13Z
dc.date.available2023-01-04T14:59:13Z
dc.date.created2021-05-18T13:25:12Z
dc.date.issued2021
dc.identifier.citationBig Data and Society. 2021, 8 (1), .
dc.identifier.issn2053-9517
dc.identifier.urihttps://hdl.handle.net/11250/3041035
dc.description.abstractArtificial Intelligence-as-a-Service (AIaaS) empowers individuals and organisations to access AI on-demand, in either tailored or ‘off-the-shelf’ forms. However, institutional separation between development, training and deployment can lead to critical opacities, such as obscuring the level of human effort necessary to produce and train AI services. Information about how, where, and for whom AI services have been produced are valuable secrets, which vendors strategically disclose to clients depending on commercial interests. This article provides a critical analysis of how AIaaS vendors manipulate the visibility of human labour in AI production based on whether the vendor relies on paid or unpaid labour to fill interstitial gaps. Where vendors are able to occlude human labour in the organisational ‘backstage,’ such as in data preparation, validation or impersonation, they do so regularly, further contributing to ongoing techno-utopian narratives of AI hype. Yet, when vendors must co-produce the AI service with the client, such as through localised AI training, they must ‘lift the curtain’, resulting in a paradoxical situation of needing to both perpetuate dominant AI hype narratives while emphasising AI’s mundane limitations.
dc.language.isoeng
dc.relation.urihttps://journals.sagepub.com/doi/full/10.1177/20539517211016026
dc.titleLifting the curtain: Strategic visibility of human labour in AI-as-a-Service
dc.typePeer reviewed
dc.typeJournal article
dc.description.versionpublishedVersion
dc.source.pagenumber14
dc.source.volume8
dc.source.journalBig Data and Society
dc.source.issue1
dc.identifier.doi10.1177/20539517211016026
dc.identifier.cristin1910523
dc.relation.projectNorges forskningsråd: 275347
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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