Making Arguments with Data: Resisting Appropriation and Assumption of Access/Reason in Machine Learning Training Processes

Kurzanzeige
dc.contributor.authorSavic, Selena
dc.contributor.authorMartins, Yann Patrick
dc.date.accessioned2023-11-13T14:48:51Z
dc.date.available2023-11-13T14:48:51Z
dc.date.issued2023
dc.description.abstractThis article presents an approach to practicing ethics when working with large datasets and designing data representations. Inspired by feminist critique of technoscience and recent problematizations of digital literacy, we argue that machine learning models can be navigated in a multi-narrative manner when access to training data is well articulated and understood. We programmed and used web-based interfaces to sort, organize, and explore a community-run digital archive of radio signals. An additional perspective on the question of working with datasets is offered from the experience of teaching image synthesis with freely accessible online tools. We hold that the main challenge to social transformations related to digital technologies comes from lingering forms of colonialism and extractive relationships that easily move in and out of the digital domain. To counter both the unfounded narratives of techno-optimismand the universalizing critique of technology, we discuss an approachto data and networks that enables a situated critique of datafication and correlationism from within.
dc.description.sponsorshipThis publication has been funded by the Federal Ministry of Education and Research of Germany (BMBF) (grant no.: 16DII121, 16DII122, 16DII123, 16DII124, 16DII125, 16DII126, 16DII127, 16DII128 – “Deutsches Internet-Institut”).
dc.identifier.citationSavic, S., & Martins, Y. P. (2023). Making Arguments with Data: Resisting Appropriation and Assumption of Access/Reason in Machine Learning Training Processes. Weizenbaum Journal of the Digital Society, 3(2). https://doi.org/10.34669/WI.WJDS/3.2.4
dc.identifier.doihttps://doi.org/10.34669/wi.wjds/3.2.4
dc.identifier.eissn2748-5625
dc.identifier.urihttps://doi.org/10.34669/WI.WJDS/3.2.4
dc.identifier.urihttps://www.weizenbaum-library.de/handle/id/412
dc.language.isoeng
dc.publisherWeizenbaum Institute
dc.rightsopen access
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectEthics of digital tools
dc.subjectdigital equity
dc.subjectcritical data studies
dc.subjectdata observatories
dc.subjectassumption of access
dc.subjectappropriation
dc.subjectclassification
dc.subjectsituated knowledge
dc.subjectmachine learning
dc.subjectartificial intelligence
dc.subject.ddc004 Informatik
dc.titleMaking Arguments with Data: Resisting Appropriation and Assumption of Access/Reason in Machine Learning Training Processes
dc.typeArticle
dc.type.statuspublishedVersion
dcmi.typeText
dcterms.bibliographicCitation.issue3
dcterms.bibliographicCitation.issue3
dcterms.bibliographicCitation.journaltitleWeizenbaum Journal of the Digital Society
dcterms.bibliographicCitation.originalpublisherplaceBerlin
dcterms.bibliographicCitation.volume2
Dateien
Originalbündel
Gerade angezeigt 1 - 1 von 1
Vorschaubild
Name:
WJDS_3.2.4_Savic-Martins_2023_Making-Arguments-with-Data.pdf
Größe:
705.32 KB
Format:
Adobe Portable Document Format
Beschreibung:
Herunterladen
Sammlungen
Vol. 3 No. 2 (2023)