mlsanityData to 14 Oct 2026

Data-Centric Artificial Intelligence

arXiv 2212.11854 ↗First posted 22 Dec 2022cs.AIcited by 31

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Abstract (arXiv)

Data-centric artificial intelligence (data-centric AI) represents an emerging paradigm emphasizing that the systematic design and engineering of data is essential for building effective and efficient AI-based systems. The objective of this article is to introduce practitioners and researchers from the field of Information Systems (IS) to data-centric AI. We define relevant terms, provide key characteristics to contrast the data-centric paradigm to the model-centric one, and introduce a framework for data-centric AI. We distinguish data-centric AI from related concepts and discuss its longer-term implications for the IS community.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 14 of its 69 references that are in the map. First posted on arXiv 22 Dec 2022.

Builds on

14 of 69 references.

References · 14

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2018UMAP: Uniform Manifold Approximation and Projection for Dimension Reductioncited by 2,898
  3. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,811
  4. 2020Learning under Concept Drift: A Reviewcited by 477
  5. 2021Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarkscited by 395
  6. 2022Machine Learning Operations (MLOps): Overview, Definition, and Architecturecited by 128
  7. 2021Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspectivecited by 116
  8. 2019Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?cited by 64
  9. 2021A Data Quality-Driven View of MLOpscited by 31
  10. 2022The Principles of Data-Centric AI (DCAI)cited by 26
  11. 2020DEAL: Deep Evidential Active Learning for Image Classificationcited by 22
  12. 2020Artificial Intelligence Governance for Businessescited by 8
2 more
  1. 2020Switching Scheme: A Novel Approach for Handling Incremental Concept Drift in Real-World Data Setscited by 4
  2. 2020Machine Learning in Artificial Intelligence: Towards a Common Understandingcited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 6; 2024: 7; 2025: 13; 2026: 5.

31 papers.

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  1. 2023Data-centric Artificial Intelligence: A Surveycited by 203
  2. 2023Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generationcited by 90
  3. 2023Data-centric AI: Perspectives and Challengescited by 43
  4. 2023Data-centric Graph Learning: A Surveycited by 22
  5. 2024The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspectivecited by 15
  6. 2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQLcited by 11
  7. 2024FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examinationcited by 10
  8. 2024Data-Juicer Sandbox: A Feedback-Driven Suite for Multimodal Data-Model Co-developmentcited by 10
  9. 2024Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspectivecited by 7
  10. 2023Transition Role of Entangled Data in Quantum Machine Learningcited by 5
  11. 2025General Information Metrics for Improving AI Model Training Efficiencycited by 4
  12. 2024Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecastingcited by 3
19 more
  1. 2025Med-CRAFT: An Information System for Explainable and Configurable Construction of Multimodal Medical QA Datasetscited by 2
  2. 2026Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Developmentcited by 2
  3. 2023UMLS-KGI-BERT: Data-Centric Knowledge Integration in Transformers for Biomedical Entity Recognitioncited by 1
  4. 2024Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Modelscited by 1
  5. 2025Analytics Modelling over Multiple Datasets using Vector Embeddingscited by 1
  6. 2025Data Quality Challenges in Retrieval-Augmented Generationcited by 1
  7. 2025End-to-End Data Quality-Driven Framework for Machine Learning in Production Environmentcited by 1
  8. 2026Key Considerations for Domain Expert Involvement in LLM Design and Evaluation: An Ethnographic Studycited by 1
  9. 2024Fair Overlap Number of Balls (Fair-ONB): A Data-Morphology-based Undersampling Method for Bias Reductioncited by 0
  10. 2025From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eSciencecited by 0
  11. 2025Out-of-distribution data supervision towards biomedical semantic segmentationcited by 0
  12. 2025Do Edges Matter? Investigating Edge-Enhanced Pre-Training for Medical Image Segmentationcited by 0
  13. 2025Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learningcited by 0
  14. 2025Optimizing Class Distributions for Bias-Aware Multi-Class Learningcited by 0
  15. 2025A Data-Centric Perspective on the Influence of Image Data Quality in Machine Learning Modelscited by 0
  16. 2025AI Application Operations -- A Socio-Technical Framework for Data-driven Organizationscited by 0
  17. 2026QuAIL: Quality-Aware Inertial Learning for Robust Training under Data Corruptioncited by 0
  18. 2026Crowdsourcing of Real-world Image Annotation via Visual Propertiescited by 0
  19. 2026Collaborative Synthetic Data for Privacy-Preserving Financial Fraud Detection Across Organizational Siloscited by 0