mlsanityData to 7 Oct 2026

The Principles of Data-Centric AI (DCAI)

arXiv 2211.14611 ↗First posted 26 Nov 2022cs.LG cs.AI cs.HCcited by 26

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Abstract (arXiv)

Data is a crucial infrastructure to how artificial intelligence (AI) systems learn. However, these systems to date have been largely model-centric, putting a premium on the model at the expense of the data quality. Data quality issues beset the performance of AI systems, particularly in downstream deployments and in real-world applications. Data-centric AI (DCAI) as an emerging concept brings data, its quality and its dynamism to the forefront in considerations of AI systems through an iterative and systematic approach. As one of the first overviews, this article brings together data-centric perspectives and concepts to outline the foundations of DCAI. It specifically formulates six guiding principles for researchers and practitioners and gives direction for future advancement of DCAI.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). Builds on 8 of its 40 references that are in the map. First posted on arXiv 26 Nov 2022.

Builds on

8 of 40 references.

References · 8

  1. 2018Datasheets for Datasetscited by 1,371
  2. 2018Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationcited by 136
  3. 2020Human-centered Explainable AI: Towards a Reflective Sociotechnical Approachcited by 90
  4. 2021A Data-Centric Approach for Training Deep Neural Networks with Less Datacited by 28
  5. 2021What can Data-Centric AI Learn from Data and ML Engineering?cited by 24
  6. 2021Data Excellence for AI: Why Should You Carecited by 14
  7. 2020Data Vision: Learning to See Through Algorithmic Abstractioncited by 11
  8. 2021Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AIcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 7; 2024: 7; 2025: 7; 2026: 4.

26 papers.

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  1. 2024Croissant: A Metadata Format for ML-Ready Datasetscited by 79
  2. 2023Data-centric AI: Perspectives and Challengescited by 43
  3. 2022Data-Centric Artificial Intelligencecited by 31
  4. 2023Better, Not Just More: Data-Centric Machine Learning for Earth Observationcited by 19
  5. 2024Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AIcited by 11
  6. 2024Multimodal Data Curation via Object Detection and Filter Ensemblescited by 8
  7. 2023Data Optimization in Deep Learning: A Surveycited by 6
  8. 2024"This is not a data problem": Algorithms and Power in Public Higher Education in Canadacited by 6
  9. 2023Transition Role of Entangled Data in Quantum Machine Learningcited by 5
  10. 2023A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentationcited by 4
  11. 2023The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluationcited by 4
  12. 2023SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)atacited by 3
14 more
  1. 2025Core-Set Selection for Data-efficient Land Cover Segmentationcited by 3
  2. 2024What About the Data? A Mapping Study on Data Engineering for AI Systemscited by 2
  3. 2026From Ground Truth to Measurement: A Statistical Framework for Human Labelingcited by 2
  4. 2024A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspectivecited by 1
  5. 2024Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Modelscited by 1
  6. 2025Lost in the Pipeline: How Well Do Large Language Models Handle Data Preparation?cited by 1
  7. 2025BTPD: A Multilingual Hand-curated Dataset of Bengali Transnational Political Discourse Across Online Communitiescited by 0
  8. 2025From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eSciencecited by 0
  9. 2025DUSTrack: Semi-automated point tracking in ultrasound videoscited by 0
  10. 2025AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AIcited by 0
  11. 2025Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Datacited by 0
  12. 2026Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscurescited by 0
  13. 2026Data Bias Mitigation under Coverage Constraints & The Price of Fairnesscited by 0
  14. 2026Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systemscited by 0