Book part:
The Quest for Workable Data. Building Machine Learning Algorithms from Public Sector Archives

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dc.contributor.editorSudmann, Andreas
dc.creatorReutter, Lisa
dc.creatorSpilker, Hendrik Storstein
dc.date.accessioned2020-03-10T12:49:25Z
dc.date.available2020-03-10T12:49:25Z
dc.date.issued2019
dc.identifier.doi10.25969/mediarep/13551
dc.identifier.urihttps://mediarep.org/handle/doc/14477
dc.languageeng
dc.publishertranscript
dc.publisher.placeBielefeld
dc.relation.isPartOfisbn:978-3-8394-4719-2
dc.relation.isPartOfdoi:https://doi.org/10.25969/mediarep/13536
dc.rightsCreative Commons Attribution Non Commercial No Derivatives 4.0 Generic
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectKünstliche Intelligenzde
dc.subjectDemokratiede
dc.subjectPolitikde
dc.subjectOpen Datade
dc.subjectNorwegende
dc.subject.ddcddc:004
dc.subject.ddcddc:320
dc.titleThe Quest for Workable Data. Building Machine Learning Algorithms from Public Sector Archivesen
dc.typebookPart
dc.type.statuspublishedVersion
dcterms.bibliographicCitationLisa Reutter und Hendrik Storstein Spilker: The Quest for Workable Data. Building Machine Learning Algorithms from Public Sector Archives. In: Andreas Sudmann (Hg.): The democratization of artificial intelligence. Net politics in the era of learning algorithms. Bielefeld: transcript (2019), S. 95–107. DOI: http://dx.doi.org/10.25969/mediarep/13551.
dspace.entity.typeBookParten
local.coverpage2021-05-29T01:08:00
local.source.booktitleThe democratization of artificial intelligence. Net politics in the era of learning algorithms
local.source.epage107
local.source.spage95

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