Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/315720 
Year of Publication: 
2024
Citation: 
[Journal:] Business & Information Systems Engineering [ISSN:] 1867-0202 [Volume:] 66 [Issue:] 4 [Publisher:] Springer Fachmedien Wiesbaden [Place:] Wiesbaden [Year:] 2024 [Pages:] 507-515
Publisher: 
Springer Fachmedien Wiesbaden, Wiesbaden
Abstract: 
Abstract Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm that emphasizes the importance of enhancing data systematically and at scale to  build effective and efficient AI-based systems. The novel paradigm complements recent model-centric AI, which focuses on improving the performance of AI-based systems based on changes in the model using a fixed set of data. The objective of this article is to introduce practitioners and researchers from the field of Business and Information Systems Engineering (BISE) to data-centric AI. The paper defines relevant terms, provides key characteristics to contrast the paradigm of data-centric AI with the model-centric one, and introduces a framework to illustrate the different dimensions of data-centric AI. In addition, an overview of available tools for data-centric AI is presented and this novel paradigm is differenciated from related concepts. Finally, the paper discusses the longer-term implications of data-centric AI for the BISE community.
Subjects: 
Data-centric artificial intelligence
Data quality
Data work
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article
Document Version: 
Published Version
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