Data-Centric Artificial Intelligence

被引：25

作者：

Jakubik, Johannes ^{[1
]}

Voessing, Michael ^{[1
]}

Kuehl, Niklas ^{[2
]}

Walk, Jannis ^{[1
]}

Satzger, Gerhard ^{[1
]}

机构：

[1] Karlsruhe Inst Technol, Kaiserstr 12, D-76131 Karlsruhe, Germany

[2] Univ Bayreuth, Univ Str 30, D-95447 Bayreuth, Germany

来源：

BUSINESS & INFORMATION SYSTEMS ENGINEERING | 2024年 / 66卷 / 04期

关键词：

Data-centric artificial intelligence; Data quality; Data work; BIG DATA; ANALYTICS;

D O I：

10.1007/s12599-024-00857-8

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：507 / 515

页数：9

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