Johannes Jakubik, Michael Vössing, Niklas Kühl, Jannis Walk, Gerhard Satzger
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引用次数: 0
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.
期刊介绍：
BISE (Business & Information Systems Engineering) is an international scholarly journal that undergoes double-blind peer review. It publishes scientific research on the effective and efficient design and utilization of information systems by individuals, groups, enterprises, and society to enhance social welfare. Information systems are viewed as socio-technical systems involving tasks, people, and technology. Research in the journal addresses issues in the analysis, design, implementation, and management of information systems.
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