# The Principles of Data-Centric AI (DCAI)

arXiv: https://arxiv.org/abs/2211.14611 · HTML: https://mlsanity.com/paper/2211.14611/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 26 Nov 2022.

## Builds on (most cited first, 8 of 8)

- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [Human-centered Explainable AI: Towards a Reflective Sociotechnical Approach](https://mlsanity.com/paper/2002.01092/) (2020)
- [A Data-Centric Approach for Training Deep Neural Networks with Less Data](https://mlsanity.com/paper/2110.03613/) (2021)
- [What can Data-Centric AI Learn from Data and ML Engineering?](https://mlsanity.com/paper/2112.06439/) (2021)
- [Data Excellence for AI: Why Should You Care](https://mlsanity.com/paper/2111.10391/) (2021)
- [Data Vision: Learning to See Through Algorithmic Abstraction](https://mlsanity.com/paper/2002.03387/) (2020)
- [Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI](https://mlsanity.com/paper/2112.03837/) (2021)

## Built on by (most cited first, 26 of 26)

- [Croissant: A Metadata Format for ML-Ready Datasets](https://mlsanity.com/paper/2403.19546/) (2024)
- [Data-centric AI: Perspectives and Challenges](https://mlsanity.com/paper/2301.04819/) (2023)
- [Data-Centric Artificial Intelligence](https://mlsanity.com/paper/2212.11854/) (2022)
- [Better, Not Just More: Data-Centric Machine Learning for Earth Observation](https://mlsanity.com/paper/2312.05327/) (2023)
- [Dissecting Sample Hardness: A Fine-Grained Analysis of Hardness Characterization Methods for Data-Centric AI](https://mlsanity.com/paper/2403.04551/) (2024)
- [Multimodal Data Curation via Object Detection and Filter Ensembles](https://mlsanity.com/paper/2401.12225/) (2024)
- [Data Optimization in Deep Learning: A Survey](https://mlsanity.com/paper/2310.16499/) (2023)
- ["This is not a data problem": Algorithms and Power in Public Higher Education in Canada](https://mlsanity.com/paper/2403.13969/) (2024)
- [Transition Role of Entangled Data in Quantum Machine Learning](https://mlsanity.com/paper/2306.03481/) (2023)
- [A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation](https://mlsanity.com/paper/2309.03906/) (2023)
- [The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation](https://mlsanity.com/paper/2311.16782/) (2023)
- [SoUnD Framework: Analyzing (So)cial Representation in (Un)structured (D)ata](https://mlsanity.com/paper/2311.17259/) (2023)
- [Core-Set Selection for Data-efficient Land Cover Segmentation](https://mlsanity.com/paper/2505.01225/) (2025)
- [What About the Data? A Mapping Study on Data Engineering for AI Systems](https://mlsanity.com/paper/2402.05156/) (2024)
- [From Ground Truth to Measurement: A Statistical Framework for Human Labeling](https://mlsanity.com/paper/2604.07591/) (2026)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [Deriva-ML: A Continuous FAIRness Approach to Reproducible Machine Learning Models](https://mlsanity.com/paper/2407.01608/) (2024)
- [Lost in the Pipeline: How Well Do Large Language Models Handle Data Preparation?](https://mlsanity.com/paper/2511.21708/) (2025)
- [BTPD: A Multilingual Hand-curated Dataset of Bengali Transnational Political Discourse Across Online Communities](https://mlsanity.com/paper/2506.06813/) (2025)
- [From Data to Decision: Data-Centric Infrastructure for Reproducible ML in Collaborative eScience](https://mlsanity.com/paper/2506.16051/) (2025)
- [DUSTrack: Semi-automated point tracking in ultrasound videos](https://mlsanity.com/paper/2507.14368/) (2025)
- [AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI](https://mlsanity.com/paper/2508.02680/) (2025)
- [Draw a Portrait of Your Graph Data: An Instance-Level Profiling Framework for Graph-Structured Data](https://mlsanity.com/paper/2509.12094/) (2025)
- [Bureaucratic Silences: What the Canadian AI Register Reveals, Omits, and Obscures](https://mlsanity.com/paper/2604.15514/) (2026)
- [Data Bias Mitigation under Coverage Constraints & The Price of Fairness](https://mlsanity.com/paper/2606.20461/) (2026)
- [Fairness Theatre: Evaluating Post-Hoc Fairness Interventions in Vendor-Controlled Early Warning Systems](https://mlsanity.com/paper/2609.38552/) (2026)

## 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.