Feeding the machine: Practitioner experiences of efforts to overcome AI's data dilemma.
This paper examines the human implications of AI's 'data dilemma' in three different and contrasting sectors: pharmaceuticals, higher education, and the arts. The 'data dilemma' refers to the challenge of obtaining sufficient and suitable data to effectively train AI algorithms. The research, conduc...
| Publicado en: | Big Data & Society Vol. 12; no. 4; pp. 1 - 16 |
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| Autores principales: | , , , , |
| Formato: | Artículo |
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Sage Publications Inc.
Dec2025
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=191630889&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 191630889 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 20539517 KG5N jtl: Big Data & Society issn: 20539517 maglogo: Y pubinfo: dt: Dec2025 vid: 12 iid: 4 pid: 344 pub: Sage Publications Inc. artinfo: ui: 191630889 10.1177/20539517251396092 ppf: 1 ppct: 15 formats: tig: atl: Feeding the machine: Practitioner experiences of efforts to overcome AI's data dilemma. aug: au: Bates, Jo Fratczak, Monika Kennedy, Helen Perea, Itzelle Medina Ochu, Erinma affil: School of Information, Journalism and Communication, University of Sheffield, UK School of Sociological Studies, Politics and International Relations, University of Sheffield, Sheffield, UK Faculty of Arts, Creative Industries, and Education, University of West England, Bristol, UK su: Artificial intelligence Data extraction Drugs Cultural industries Higher education sug: subj: Artificial intelligence Data extraction Drugs Cultural industries Higher education keyword: AI, machine learning data dilemma data inputs data work practitioners ab: This paper examines the human implications of AI's 'data dilemma' in three different and contrasting sectors: pharmaceuticals, higher education, and the arts. The 'data dilemma' refers to the challenge of obtaining sufficient and suitable data to effectively train AI algorithms. The research, conducted in the UK, involved interviews, focus groups, and observations with 65 practitioners employed across these three sectors. The findings reveal that addressing the data dilemma often involves practitioners being pressured to generate data for AI, either passively in the context of data extractivism or actively by engaging in new forms of data production. We explore how this pressure to 'feed the machine' manifests differently in each sector, and how modes of resistance to these emergent data practices vary across sectors. We observe that the push to resolve the data dilemma is fundamentally driven by capitalist and technological solutionist values; values that often conflict with those of practitioners who are expected to adapt their practices in the service of AI-driven capitalism. We conclude with a call for exploring different approaches to AI development that align with alternative value systems. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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