Conference item
Challenges in data preservation for AI and ML systems
- Abstract:
- The 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Version of record, pdf, 342.9KB, Terms of use)
-
- Publisher copy:
- 10.18420/inf2024_38
Authors
- Publisher:
- Gesellschaft für Information
- Host title:
- INFORMATIK 2024 - Lock in or log out? Wie digitale Souveränität gelingt
- Pages:
- 511-522
- Series:
- Lecture Notes in Informatics
- Series number:
- P352
- Publication date:
- 2024-11-10
- Acceptance date:
- 2024-06-24
- Event title:
- 8th International Workshop on Annotation of useR Data for UbiquitOUs Systems @ INFORMATIK 2024
- Event location:
- Wiesbaden, Germany
- Event website:
- https://arduous.eu/past-editions/arduous-2024/
- Event start date:
- 2024-09-24
- Event end date:
- 2024-09-24
- DOI:
- EISSN:
-
1617-5468
- ISSN:
-
2944-7682
- ISBN:
- 9783885797463
- Language:
-
English
- Keywords:
- Pubs id:
-
2083829
- Local pid:
-
pubs:2083829
- Deposit date:
-
2025-02-24
- ARK identifier:
Terms of use
- Copyright holder:
- Tonkin and Tourte
- Copyright date:
- 2024
- Rights statement:
- © 2024 The Authors. This is an open access article under a Creative Commons license.
- Licence:
- CC Attribution-ShareAlike (CC BY-SA)
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