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Title
Data Sovereignty for AI Pipelines: Lessons Learned from an Industrial Project at Mondragon Corporation
Author
Larrinaga, FelixMondragon Unibertsitatea
Author (from another institution)
Altendeitering, Marcel
Pampus, Julia
Legaristi Labajos, Jon
Howar, Falk
Research Group
Ingeniería del software y sistemas
Published Date
2022
Publisher
ACM
Keywords
data sovereignty
collaborative AI
lessons learned
AI engineering
Abstract
The 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 s ... [+]
The 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. [-]
URI
https://hdl.handle.net/20.500.11984/5878
Publisher’s version
https://doi.org/10.1145/3522664.3528593
ISBN
978-1-4503-9275-4
Published at
CAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI  May 2022. Pp. 193-204. IEEE, 2022
Document type
Conference paper
Version
Postprint – Accepted Manuscript
Rights
© 2022 The Authors
Access
Open Access
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  • Conferences - Engineering [234]

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