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dc.contributor.authorLarrinaga, Felix
dc.contributor.otherAltendeitering, Marcel
dc.contributor.otherPampus, Julia
dc.contributor.otherLegaristi Labajos, Jon
dc.contributor.otherHowar, Falk
dc.date.accessioned2022-11-23T10:05:54Z
dc.date.available2022-11-23T10:05:54Z
dc.date.issued2022
dc.identifier.isbn978-1-4503-9275-4en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=167653en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5878
dc.description.abstractThe establishment of collaborative AI pipelines, in which multiple organizations share their data and models, is often complicated by lengthy data governance processes and legal clarifications. Data sovereignty solutions, which ensure data is being used under agreed terms and conditions, are promising to overcome these problems. However, there is limited research on their applicability in AI pipelines. In this study, we extended an existing AI pipeline at Mondragon Corporation, in which sensor data is collected and subsequently forwarded to a data quality service provider with a data sovereignty component. By systematically reflecting and generalizing our experiences during the twelve-month action research project, we formulated ten lessons learned, four benefits, and three barriers to data-sovereign AI pipelines that can inform further research and custom implementations. Our results show that a data sovereignty component can help reduce existing barriers and increase the success of collaborative data science initiatives.en
dc.description.sponsorshipComisión Europeaes
dc.language.isoengen
dc.publisherACMen
dc.rights© 2022 The Authorsen
dc.subjectdata sovereigntyen
dc.subjectcollaborative AIen
dc.subjectlessons learneden
dc.subjectAI engineeringen
dc.titleData Sovereignty for AI Pipelines: Lessons Learned from an Industrial Project at Mondragon Corporationen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceCAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AIen
local.contributor.groupIngeniería del software y sistemases
local.description.peerreviewedtrueen
local.description.publicationfirstpage193en
local.description.publicationlastpage204en
local.identifier.doihttps://doi.org/10.1145/3522664.3528593en
local.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/825030/EU/Digital Reality in Zero Defect Manufacturing/QU4LITYen
local.contributor.otherinstitutionhttps://ror.org/058kjq542de
local.contributor.otherinstitutionhttps://ror.org/01k97gp34en
local.source.detailsMay 2022. Pp. 193-204. IEEE, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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