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Challenges in Data Preservation for AI and ML Systems

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dc.contributor.authorTonkin, Emma L.
dc.contributor.authorTourte, Gregory J. L.
dc.contributor.editorKlein, Maike
dc.contributor.editorKrupka, Daniel
dc.contributor.editorWinter, Cornelia
dc.contributor.editorGergeleit, Martin
dc.contributor.editorMartin, Ludger
dc.date.accessioned2024-10-21T18:24:26Z
dc.date.available2024-10-21T18:24:26Z
dc.date.issued2024
dc.description.abstractThe management and preservation of machine learning (ML) and artificial intelligence (AI) data is increasingly a concern for research institutions, as well as for institutions and industry organisations making use of this type of data and method. This paper summarises key issues in this area, presenting the case that there are significant benefits to the industry in developing best practices and joint standards in this area, and identifying the benefits of this approach, as well as highlighting risks and a current paucity of best practice in the area.en
dc.identifier.doi10.18420/inf2024_38
dc.identifier.isbn978-3-88579-746-3
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/45197
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofINFORMATIK 2024
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-352
dc.subjectdata preservation
dc.subjectdata management
dc.subjectartificial intelligence
dc.subjectmachine learning
dc.subjectbest practices
dc.titleChallenges in Data Preservation for AI and ML Systemsen
dc.typeText/Conference Paper
gi.citation.endPage522
gi.citation.publisherPlaceBonn
gi.citation.startPage511
gi.conference.date24.-26. September 2024
gi.conference.locationWiesbaden
gi.conference.sessiontitle8th International Workshop on Annotation of useR Data for UbiquitOUs Systems

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