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Data-Centric Artificial Intelligence

Author

Listed:
  • Johannes Jakubik

    (Karlsruhe Institute of Technology)

  • Michael Vössing

    (Karlsruhe Institute of Technology)

  • Niklas Kühl

    (University of Bayreuth)

  • Jannis Walk

    (Karlsruhe Institute of Technology)

  • Gerhard Satzger

    (Karlsruhe Institute of Technology)

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.

Suggested Citation

  • Johannes Jakubik & Michael Vössing & Niklas Kühl & Jannis Walk & Gerhard Satzger, 2024. "Data-Centric Artificial Intelligence," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 66(4), pages 507-515, August.
    • Handle: RePEc:spr:binfse:v:66:y:2024:i:4:d:10.1007_s12599-024-00857-8
    DOI: 10.1007/s12599-024-00857-8
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    References listed on IDEAS

    as
    1. Niklas Kühl & Max Schemmer & Marc Goutier & Gerhard Satzger, 2022. "Artificial intelligence and machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2235-2244, December.
    2. Joshua Holstein & Max Schemmer & Johannes Jakubik & Michael Vössing & Gerhard Satzger, 2023. "Sanitizing data for analysis: Designing systems for data understanding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-18, December.
    3. Kühl, Niklas & Schemmer, Max & Goutier, Marc & Satzger, Gerhard, 2022. "Artificial intelligence and machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 135656, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    Full references (including those not matched with items on IDEAS)

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